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+     <use xlink:href="#ArialMT-6e" transform="translate(444.580078 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(500.195312 0)"/>
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+     <use xlink:href="#ArialMT-65" transform="translate(816.992188 0)"/>
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+     <use xlink:href="#ArialMT-6f" transform="translate(928.222656 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(983.837891 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(1017.138672 0)"/>
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+   <g id="matplotlib.axis_3"/>
+   <g id="matplotlib.axis_4"/>
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+   <g id="text_6">
+    <!-- Male -->
+    <g style="fill: #262626" transform="translate(368.374913 415.543883) scale(0.1 -0.1)">
+     <defs>
+      <path id="ArialMT-4d" d="M 475 0 
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+     <use xlink:href="#ArialMT-4d"/>
+     <use xlink:href="#ArialMT-61" transform="translate(83.300781 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(138.916016 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(161.132812 0)"/>
+    </g>
+   </g>
+   <g id="text_7">
+    <!-- 61.6% -->
+    <g style="fill: #262626" transform="translate(439.1278 391.471734) scale(0.1 -0.1)">
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+     <use xlink:href="#ArialMT-36"/>
+     <use xlink:href="#ArialMT-31" transform="translate(55.615234 0)"/>
+     <use xlink:href="#ArialMT-2e" transform="translate(111.230469 0)"/>
+     <use xlink:href="#ArialMT-36" transform="translate(139.013672 0)"/>
+     <use xlink:href="#ArialMT-25" transform="translate(194.628906 0)"/>
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+   </g>
+   <g id="text_8">
+    <!-- Female -->
+    <g style="fill: #262626" transform="translate(668.374802 309.62643) scale(0.1 -0.1)">
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+     <use xlink:href="#ArialMT-46"/>
+     <use xlink:href="#ArialMT-65" transform="translate(61.083984 0)"/>
+     <use xlink:href="#ArialMT-6d" transform="translate(116.699219 0)"/>
+     <use xlink:href="#ArialMT-61" transform="translate(200 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(255.615234 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(277.832031 0)"/>
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+   </g>
+   <g id="text_9">
+    <!-- 38.4% -->
+    <g style="fill: #262626" transform="translate(590.942228 333.698578) scale(0.1 -0.1)">
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+      <path id="ArialMT-33" d="M 269 1209 
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+     <use xlink:href="#ArialMT-38" transform="translate(55.615234 0)"/>
+     <use xlink:href="#ArialMT-2e" transform="translate(111.230469 0)"/>
+     <use xlink:href="#ArialMT-34" transform="translate(139.013672 0)"/>
+     <use xlink:href="#ArialMT-25" transform="translate(194.628906 0)"/>
+    </g>
+   </g>
+   <g id="text_10">
+    <!-- Distribution by Sex -->
+    <g style="fill: #262626" transform="translate(479.192201 184.796141) scale(0.12 -0.12)">
+     <defs>
+      <path id="ArialMT-78" d="M 47 0 
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+     <use xlink:href="#ArialMT-73" transform="translate(94.433594 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(144.433594 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(172.216797 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(205.517578 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(227.734375 0)"/>
+     <use xlink:href="#ArialMT-75" transform="translate(283.349609 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(338.964844 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(366.748047 0)"/>
+     <use xlink:href="#ArialMT-6f" transform="translate(388.964844 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(444.580078 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(500.195312 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(527.978516 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(583.59375 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(633.59375 0)"/>
+     <use xlink:href="#ArialMT-53" transform="translate(661.376953 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(728.076172 0)"/>
+     <use xlink:href="#ArialMT-78" transform="translate(783.691406 0)"/>
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+ </g>
+</svg>
diff --git a/assets/identified_category_distribution.csv b/assets/identified_category_distribution.csv
index fd15193..44c8e5d 100644
--- a/assets/identified_category_distribution.csv
+++ b/assets/identified_category_distribution.csv
@@ -1,13 +1,13 @@
-province,compose,simple
-AUTRES,0.2062165520477412,0.7937834479522587
-BANDUNDU,0.6269061385346485,0.3730938614653515
-BAS-CONGO,0.09081330148566008,0.90918669851434
-EQUATEUR,0.12423822403788959,0.8757617759621105
-KASAI-OCCIDENTAL,0.2612655252892886,0.7387344747107114
-KASAI-ORIENTAL,0.07622377139542966,0.9237762286045703
-KATANGA,0.18062352012628255,0.8193764798737174
-KINSHASA,0.07679244621346286,0.9232075537865372
-MANIEMA,0.4611502742287561,0.5388497257712439
-NORD-KIVU,0.11962561930536533,0.8803743806946347
-ORIENTALE,0.16090483213325235,0.8390951678667476
-SUD-KIVU,0.409646629226467,0.590353370773533
+compose,simple
+0.2062165520477412,0.7937834479522587
+0.6269061385346485,0.3730938614653515
+0.09081330148566008,0.90918669851434
+0.12423822403788959,0.8757617759621105
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+0.4611502742287561,0.5388497257712439
+0.11962561930536533,0.8803743806946347
+0.16090483213325235,0.8390951678667476
+0.409646629226467,0.590353370773533
diff --git a/assets/identified_category_distribution_by_province.svg b/assets/identified_category_distribution_by_province.svg
index 0308c2e..5bc9294 100644
--- a/assets/identified_category_distribution_by_province.svg
+++ b/assets/identified_category_distribution_by_province.svg
@@ -6,7 +6,7 @@
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    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2025-09-28T16:57:46.163844</dc:date>
+    <dc:date>2025-09-28T23:42:04.307504</dc:date>
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     <dc:creator>
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+     <use xlink:href="#ArialMT-20" transform="translate(344.53125 0)"/>
+     <use xlink:href="#ArialMT-44" transform="translate(372.314453 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(444.53125 0)"/>
+     <use xlink:href="#ArialMT-73" transform="translate(466.748047 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(516.748047 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(544.53125 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(577.832031 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(600.048828 0)"/>
+     <use xlink:href="#ArialMT-75" transform="translate(655.664062 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(711.279297 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(739.0625 0)"/>
+     <use xlink:href="#ArialMT-6f" transform="translate(761.279297 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(816.894531 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(872.509766 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(900.292969 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(955.908203 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(1005.908203 0)"/>
+     <use xlink:href="#ArialMT-47" transform="translate(1033.691406 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(1111.474609 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(1167.089844 0)"/>
+     <use xlink:href="#ArialMT-64" transform="translate(1222.705078 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(1278.320312 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(1333.935547 0)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_2">
+   <g id="matplotlib.axis_3"/>
+   <g id="matplotlib.axis_4"/>
+   <g id="patch_5">
+    <path d="M 523.046635 241.068529 
+C 505.719834 241.068529 488.600518 244.85497 472.889919 252.162165 
+C 457.17932 259.46936 443.2537 270.122303 432.090194 283.373498 
+C 420.926689 296.624693 412.79266 312.156779 408.258857 328.879896 
+C 403.725054 345.603013 402.90006 363.116651 405.841764 380.191908 
+C 408.783468 397.267164 415.421417 413.495096 425.289837 427.737016 
+C 435.158258 441.978936 448.020806 453.893758 462.974939 462.645439 
+C 477.929072 471.39712 494.616645 476.776061 511.866721 478.404832 
+C 529.116797 480.033603 546.516244 477.873196 562.844173 472.075201 
+L 523.046635 360 
+z
+" style="fill: #1f77b4; stroke: #ffffff; stroke-linejoin: miter"/>
+   </g>
+   <g id="patch_6">
+    <path d="M 562.844173 472.075201 
+C 580.886193 465.668539 597.117215 454.998967 610.15292 440.976477 
+C 623.188624 426.953988 632.647568 409.988899 637.723011 391.528139 
+C 642.798454 373.067379 643.341882 353.651135 639.306947 334.93539 
+C 635.272011 316.219646 626.776781 298.75205 614.5458 284.022388 
+L 523.046635 360 
+z
+" style="fill: #ff7f0e; stroke: #ffffff; stroke-linejoin: miter"/>
+   </g>
+   <g id="patch_7">
+    <path d="M 614.5458 284.022388 
+C 603.385693 270.58237 589.4002 259.76487 573.586548 252.341224 
+C 557.772896 244.917579 540.516097 241.068529 523.046635 241.068529 
+L 523.046635 360 
+z
+" style="fill: #2ca02c; stroke: #ffffff; stroke-linejoin: miter"/>
+   </g>
+   <g id="text_8">
+    <!-- Only Male -->
+    <g style="fill: #262626" transform="translate(349.108777 384.801723) scale(0.1 -0.1)">
+     <use xlink:href="#ArialMT-4f"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(77.783203 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(133.398438 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(155.615234 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(205.615234 0)"/>
+     <use xlink:href="#ArialMT-4d" transform="translate(233.398438 0)"/>
+     <use xlink:href="#ArialMT-61" transform="translate(316.699219 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(372.314453 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(394.53125 0)"/>
+    </g>
+   </g>
+   <g id="text_9">
+    <!-- 55.4% -->
+    <g style="fill: #262626" transform="translate(438.54715 374.700301) scale(0.1 -0.1)">
+     <defs>
+      <path id="ArialMT-35" d="M 266 1200 
+L 856 1250 
+Q 922 819 1161 601 
+Q 1400 384 1738 384 
+Q 2144 384 2425 690 
+Q 2706 997 2706 1503 
+Q 2706 1984 2436 2262 
+Q 2166 2541 1728 2541 
+Q 1456 2541 1237 2417 
+Q 1019 2294 894 2097 
+L 366 2166 
+L 809 4519 
+L 3088 4519 
+L 3088 3981 
+L 1259 3981 
+L 1013 2750 
+Q 1425 3038 1878 3038 
+Q 2478 3038 2890 2622 
+Q 3303 2206 3303 1553 
+Q 3303 931 2941 478 
+Q 2500 -78 1738 -78 
+Q 1113 -78 717 272 
+Q 322 622 266 1200 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#ArialMT-35"/>
+     <use xlink:href="#ArialMT-35" transform="translate(55.615234 0)"/>
+     <use xlink:href="#ArialMT-2e" transform="translate(111.230469 0)"/>
+     <use xlink:href="#ArialMT-34" transform="translate(139.013672 0)"/>
+     <use xlink:href="#ArialMT-25" transform="translate(194.628906 0)"/>
+    </g>
+   </g>
+   <g id="text_10">
+    <!-- Only Female -->
+    <g style="fill: #262626" transform="translate(649.190648 397.271578) scale(0.1 -0.1)">
+     <use xlink:href="#ArialMT-4f"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(77.783203 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(133.398438 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(155.615234 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(205.615234 0)"/>
+     <use xlink:href="#ArialMT-46" transform="translate(233.398438 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(294.482422 0)"/>
+     <use xlink:href="#ArialMT-6d" transform="translate(350.097656 0)"/>
+     <use xlink:href="#ArialMT-61" transform="translate(433.398438 0)"/>
+     <use xlink:href="#ArialMT-6c" transform="translate(489.013672 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(511.230469 0)"/>
+    </g>
+   </g>
+   <g id="text_11">
+    <!-- 30.6% -->
+    <g style="fill: #262626" transform="translate(577.675898 381.50204) scale(0.1 -0.1)">
+     <use xlink:href="#ArialMT-33"/>
+     <use xlink:href="#ArialMT-30" transform="translate(55.615234 0)"/>
+     <use xlink:href="#ArialMT-2e" transform="translate(111.230469 0)"/>
+     <use xlink:href="#ArialMT-36" transform="translate(139.013672 0)"/>
+     <use xlink:href="#ArialMT-25" transform="translate(194.628906 0)"/>
+    </g>
+   </g>
+   <g id="text_12">
+    <!-- Shared -->
+    <g style="fill: #262626" transform="translate(578.640539 244.160503) scale(0.1 -0.1)">
+     <use xlink:href="#ArialMT-53"/>
+     <use xlink:href="#ArialMT-68" transform="translate(66.699219 0)"/>
+     <use xlink:href="#ArialMT-61" transform="translate(122.314453 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(177.929688 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(211.230469 0)"/>
+     <use xlink:href="#ArialMT-64" transform="translate(266.845703 0)"/>
+    </g>
+   </g>
+   <g id="text_13">
+    <!-- 14.0% -->
+    <g style="fill: #262626" transform="translate(539.19402 297.989891) scale(0.1 -0.1)">
+     <use xlink:href="#ArialMT-31"/>
+     <use xlink:href="#ArialMT-34" transform="translate(55.615234 0)"/>
+     <use xlink:href="#ArialMT-2e" transform="translate(111.230469 0)"/>
+     <use xlink:href="#ArialMT-30" transform="translate(139.013672 0)"/>
+     <use xlink:href="#ArialMT-25" transform="translate(194.628906 0)"/>
+    </g>
+   </g>
+   <g id="text_14">
+    <!-- Surnames - Distribution by Gender -->
+    <g style="fill: #262626" transform="translate(430.679447 205.335661) scale(0.12 -0.12)">
+     <use xlink:href="#ArialMT-53"/>
+     <use xlink:href="#ArialMT-75" transform="translate(66.699219 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(122.314453 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(155.615234 0)"/>
+     <use xlink:href="#ArialMT-61" transform="translate(211.230469 0)"/>
+     <use xlink:href="#ArialMT-6d" transform="translate(266.845703 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(350.146484 0)"/>
+     <use xlink:href="#ArialMT-73" transform="translate(405.761719 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(455.761719 0)"/>
+     <use xlink:href="#ArialMT-2d" transform="translate(483.544922 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(516.845703 0)"/>
+     <use xlink:href="#ArialMT-44" transform="translate(544.628906 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(616.845703 0)"/>
+     <use xlink:href="#ArialMT-73" transform="translate(639.0625 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(689.0625 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(716.845703 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(750.146484 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(772.363281 0)"/>
+     <use xlink:href="#ArialMT-75" transform="translate(827.978516 0)"/>
+     <use xlink:href="#ArialMT-74" transform="translate(883.59375 0)"/>
+     <use xlink:href="#ArialMT-69" transform="translate(911.376953 0)"/>
+     <use xlink:href="#ArialMT-6f" transform="translate(933.59375 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(989.208984 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(1044.824219 0)"/>
+     <use xlink:href="#ArialMT-62" transform="translate(1072.607422 0)"/>
+     <use xlink:href="#ArialMT-79" transform="translate(1128.222656 0)"/>
+     <use xlink:href="#ArialMT-20" transform="translate(1178.222656 0)"/>
+     <use xlink:href="#ArialMT-47" transform="translate(1206.005859 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(1283.789062 0)"/>
+     <use xlink:href="#ArialMT-6e" transform="translate(1339.404297 0)"/>
+     <use xlink:href="#ArialMT-64" transform="translate(1395.019531 0)"/>
+     <use xlink:href="#ArialMT-65" transform="translate(1450.634766 0)"/>
+     <use xlink:href="#ArialMT-72" transform="translate(1506.25 0)"/>
+    </g>
+   </g>
+  </g>
+ </g>
+</svg>
diff --git a/assets/names_letter_frequencies.csv b/assets/names_letter_frequencies.csv
new file mode 100644
index 0000000..a40fa93
--- /dev/null
+++ b/assets/names_letter_frequencies.csv
@@ -0,0 +1,27 @@
+letter,Male,Female
+a,0.1726871198212362,0.1780007719968084
+b,0.06275449167118631,0.05757115683434764
+c,0.002527913112031784,0.002525362815502787
+d,0.02639274743484273,0.025798130028588412
+e,0.060460557268468315,0.05992111228866155
+f,0.004168185425527368,0.005738163668905593
+g,0.03710295718248242,0.035944081768606244
+h,0.015744753594548896,0.016324638692088497
+i,0.07320872667180656,0.07954877144283247
+j,0.00442530712700423,0.004397881604276826
+k,0.06012485644271973,0.05719911396875115
+l,0.04930645065793003,0.04598845291218479
+m,0.08281339976187696,0.08014229460267776
+n,0.08138893330151427,0.08430430794896865
+o,0.06920807306069308,0.06452478894803111
+p,0.009832203366545821,0.009371006578405026
+q,5.0822826402147366e-05,8.43622136063042e-05
+r,0.009139850680293098,0.010064380634131025
+s,0.032639239825093015,0.034139532349508485
+t,0.0277669772899704,0.027953179679053274
+u,0.06917254296038988,0.06619473621457156
+v,0.0035449558612418576,0.006171217790567778
+w,0.013512780220408454,0.014295070954872152
+x,4.796818419701171e-05,1.6707334940670683e-05
+y,0.020592394840652214,0.020809516372185803
+z,0.01138579141093724,0.012971260356926069
diff --git a/assets/names_ngrams_summary.csv b/assets/names_ngrams_summary.csv
new file mode 100644
index 0000000..97a4f8a
--- /dev/null
+++ b/assets/names_ngrams_summary.csv
@@ -0,0 +1,7 @@
+sex,position,2-grams,3-grams,4-grams
+female,prefix,"ka, ma, mu, mb, ng, ba, ki, lu, ts, bo","tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man","tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum, kavu, ngal"
+female,suffix,"ba, ga, la, ka, ma, da, go, ya, bo, na","nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu","anga, amba, ongo, umba, inga, ombo, unga, enga, anda, ungu"
+female,any,"ng, ka, mb, an, ba, ma, nd, ga, la, am","nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng","anga, amba, ongo, tshi, umba, inga, ombo, unga, anda, enga"
+male,prefix,"ka, mu, ma, ba, mb, ng, ki, lu, ts, bo","kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu","tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe, kalo, muto"
+male,suffix,"ba, ga, la, go, ka, da, bo, le, di, ma","nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele","amba, ongo, anga, umba, unga, ombo, anda, enga, onga, angu"
+male,any,"ng, ka, mb, ba, an, ma, mu, nd, am, al","nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo","amba, ongo, anga, tshi, umba, unga, ombo, anda, lung, enga"
diff --git a/assets/names_transition_probabilities.svg b/assets/names_transition_probabilities.svg
index 734b829..0f475b5 100644
--- a/assets/names_transition_probabilities.svg
+++ b/assets/names_transition_probabilities.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2025-09-28T16:59:18.671189</dc:date>
+    <dc:date>2025-09-28T23:43:46.934196</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -42,7 +42,7 @@ z
      <g id="line2d_1">
       <path d="M 185.996606 384.48 
 L 185.996606 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_1">
       <!-- a -->
@@ -99,7 +99,7 @@ z
      <g id="line2d_2">
       <path d="M 197.989819 384.48 
 L 197.989819 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_2">
       <!-- b -->
@@ -140,7 +140,7 @@ z
      <g id="line2d_3">
       <path d="M 209.983032 384.48 
 L 209.983032 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_3">
       <!-- c -->
@@ -177,7 +177,7 @@ z
      <g id="line2d_4">
       <path d="M 221.976244 384.48 
 L 221.976244 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_4">
       <!-- d -->
@@ -219,7 +219,7 @@ z
      <g id="line2d_5">
       <path d="M 233.969457 384.48 
 L 233.969457 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_5">
       <!-- e -->
@@ -259,7 +259,7 @@ z
      <g id="line2d_6">
       <path d="M 245.96267 384.48 
 L 245.96267 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_6">
       <!-- f -->
@@ -296,7 +296,7 @@ z
      <g id="line2d_7">
       <path d="M 257.955882 384.48 
 L 257.955882 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_7">
       <!-- g -->
@@ -345,7 +345,7 @@ z
      <g id="line2d_8">
       <path d="M 269.949095 384.48 
 L 269.949095 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_8">
       <!-- h -->
@@ -380,7 +380,7 @@ z
      <g id="line2d_9">
       <path d="M 281.942308 384.48 
 L 281.942308 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_9">
       <!-- i -->
@@ -408,7 +408,7 @@ z
      <g id="line2d_10">
       <path d="M 293.93552 384.48 
 L 293.93552 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_10">
       <!-- j -->
@@ -442,7 +442,7 @@ z
      <g id="line2d_11">
       <path d="M 305.928733 384.48 
 L 305.928733 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_11">
       <!-- k -->
@@ -472,7 +472,7 @@ z
      <g id="line2d_12">
       <path d="M 317.921946 384.48 
 L 317.921946 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_12">
       <!-- l -->
@@ -494,7 +494,7 @@ z
      <g id="line2d_13">
       <path d="M 329.915158 384.48 
 L 329.915158 51.84 
-" clip-path="url(#pa3f7bf4544)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#p2708b3bffc)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_13">
       <!-- m -->
@@ -540,7 +540,7 @@ z
      <g id="line2d_14">
       <path d="M 341.908371 384.48 
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diff --git a/assets/surnames_letter_frequencies.csv b/assets/surnames_letter_frequencies.csv
new file mode 100644
index 0000000..1aef163
--- /dev/null
+++ b/assets/surnames_letter_frequencies.csv
@@ -0,0 +1,27 @@
+letter,Male,Female
+a,0.10419923843992125,0.11230333493368445
+b,0.018546448249013497,0.015225074098649389
+c,0.041480767577953326,0.04405987823025858
+d,0.03868872722439995,0.026961499859145175
+e,0.13138825792008238,0.18038344738539613
+f,0.010247500256025038,0.007027086508994281
+g,0.0180527572420696,0.017807501663033867
+h,0.031508761634381516,0.03697185263156448
+i,0.09337919525658041,0.10271520299704247
+j,0.026664242619993696,0.012183083323972286
+k,0.012803255631156278,0.004848323140290566
+l,0.0509726758057992,0.06672341587576307
+m,0.03320386129622267,0.030360801411648666
+n,0.07989188838489009,0.08206189166389144
+o,0.057005660062330925,0.03761362219276409
+p,0.021467218695097115,0.011531157247822707
+q,0.0018784195453980996,0.001950967247682959
+r,0.0734505638264324,0.06822482369855525
+s,0.05242163399917173,0.0432875249054165
+t,0.03949576796436023,0.04783038894946737
+u,0.031398878017324786,0.020461941209356852
+v,0.011932256018971252,0.013217596918162086
+w,0.0020867055933035655,0.0015356192415710282
+x,0.002258056881804058,0.0006618534967773684
+y,0.013323645811687641,0.011759717016584062
+z,0.002253616045629267,0.002292394152504883
diff --git a/assets/surnames_ngrams_summary.csv b/assets/surnames_ngrams_summary.csv
new file mode 100644
index 0000000..b22331b
--- /dev/null
+++ b/assets/surnames_ngrams_summary.csv
@@ -0,0 +1,7 @@
+sex,position,2-grams,3-grams,4-grams
+female,prefix,"ma, ch, be, na, an, sa, es, jo, ju, me","mar, cha, est, chr, gra, dor, sar, rut, ben, mer","mari, chri, esth, grac, sara, dorc, ruth, rach, naom, jean"
+female,suffix,"ne, ie, te, le, ce, ia, se, el, ah, th","ine, tte, lle, rah, nce, ene, nne, her, lie, rie","ette, line, tine, elle, ther, arie, ille, rcas, ruth, arah"
+female,any,"ne, in, el, an, ie, ri, ra, li, ar, er","ine, tte, ett, mar, ari, lle, lin, eli, the, ell","ette, line, ther, mari, tine, elle, rist, chri, ance, hris"
+male,prefix,"jo, je, pa, ch, ma, da, al, ju, be, fr","jea, jos, chr, pat, mar, jon, fra, dan, cha, ben","jean, chri, jose, jona, patr, fran, mich, emma, davi, dieu"
+male,suffix,"in, el, an, ck, on, re, ce, er, se, is","ean, tin, ick, ier, ard, uel, ert, ain, iel, ise","jean, stin, rick, bert, seph, than, oise, avid, ndre, tian"
+male,any,"an, er, el, ie, in, ri, is, on, en, re","ric, sti, jea, ean, tin, ris, ier, ist, ick, jos","jean, stin, rist, rick, chri, hris, usti, bert, jose, atha"
diff --git a/assets/surnames_transition_probabilities.svg b/assets/surnames_transition_probabilities.svg
index e849c38..47a2f94 100644
--- a/assets/surnames_transition_probabilities.svg
+++ b/assets/surnames_transition_probabilities.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2025-09-28T16:59:19.901506</dc:date>
+    <dc:date>2025-09-28T23:43:48.172909</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -42,7 +42,7 @@ z
      <g id="line2d_1">
       <path d="M 185.996606 384.48 
 L 185.996606 51.84 
-" clip-path="url(#p4ad9b24a97)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
+" clip-path="url(#paae7af29b2)" style="fill: none; stroke: #cccccc; stroke-width: 0.8; stroke-linecap: round"/>
      </g>
      <g id="text_1">
       <!-- a -->
@@ -99,7 +99,7 @@ z
      <g id="line2d_2">
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diff --git a/assets/surnames_transition_probs.csv b/assets/surnames_transition_probs.csv
index 0b56068..0d38df1 100644
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+0.0,0.1627793575149433,0.0035916362881505785,0.036496892212239035,0.040730844643505654,0.1503575411693532,0.0012619623047103333,0.015429811736916198,0.0012770756257248282,0.23819394035839017,0.00021692060750216365,0.000525410159974503,0.026246059979436992,0.01374067585882558,0.020855493981119907,0.0587779279725791,0.0009934786019822454,0.00013068577583122155,0.016929586592884027,0.006149343615133057,0.04790433801209155,0.02665767572236118,0.017815938419439997,0.00039161281805206186,2.5781547612962073e-05,0.01068822952333919,0.00010534873765986227,0.1017264302202411
+0.0,0.11064837045719457,0.00019155378133028236,0.03693078718830975,0.00084374879871672,0.1936315534685894,6.319971696951493e-05,3.192563022171373e-05,0.02285614506485138,0.058747068868796735,1.1076239056512926e-05,0.0013578165996337023,0.008225084814671716,0.006192269176064638,0.0010717890145861037,0.04918110758481304,0.02873958263428148,0.0002521473244041472,0.005270335160478415,0.04403717185827366,0.20958133770996804,0.02021609090855783,8.274602118689069e-05,0.0006600135390733879,1.954630421737575e-06,0.024633555661684747,5.863891265212725e-06,0.17653570425659867
+0.0,0.06351954441846226,1.4753288139094001e-05,0.0013484505359131916,1.99169389877769e-05,0.16353651069982222,1.84416101738675e-05,3.39325627199162e-05,0.18805131193614777,0.2149259016103214,1.4753288139094001e-05,1.69662813599581e-05,0.0001386809085074836,0.00011802630511275201,0.00019253041021517672,0.07676357118092694,5.38495017076931e-05,2.2129932208641e-06,0.06875179805699196,0.010426886392304685,0.08126775004979235,0.009717990897221218,0.0003732581899190782,0.0005030871255431055,1.10649661043205e-05,0.008581250046104025,3.98338779755538e-05,0.11155772592816623
+0.0,0.018356080355773654,0.01654418269968032,0.045542802543262595,0.07255847970462294,0.18801741120403903,0.0036002028947896146,0.04503556556922605,0.002494898139737888,0.03139678907199226,0.0007679331862739315,0.011800927182003705,0.10299977587203472,0.02639873544642752,0.06185106106897243,0.0003314734644285327,0.009195144680499687,9.672891132789921e-05,0.06532858339329739,0.1206587002937256,0.050628148113196415,0.00014745260873155367,0.0076580986870819715,0.0010781734750451204,0.0062708645441355145,0.020688190815471907,0.010810045651327663,0.07974355042289408
+0.0,0.12830504252517705,1.01674063454783e-05,3.304407062280448e-05,0.00014742739200943535,0.30917049215330417,5.08370317273915e-06,7.625554759108725e-06,0.00013471813407758747,0.5037873588636906,2.541851586369575e-06,7.625554759108725e-06,0.0008693132425383947,2.541851586369575e-05,0.00019572257215045728,0.04173211934501568,5.08370317273915e-06,0.0,0.00263081639189251,5.8462586486500224e-05,2.7960367450065326e-05,0.00580050532009537,4.321147696828278e-05,0.00031010589353708815,0.0,0.002524058625264988,5.08370317273915e-06,0.004161011046886994
+0.0,0.36613233287858116,0.0001534788540245566,1.7053206002728514e-05,0.0002899045020463847,0.16732605729877217,1.7053206002728514e-05,6.821282401091406e-05,0.001892905866302865,0.4231412005457026,1.7053206002728514e-05,5.1159618008185536e-05,0.0003751705320600273,0.00042633015006821284,0.0036493860845839016,0.013250341064120055,1.7053206002728514e-05,5.1159618008185536e-05,0.0007162346521145975,0.0006309686221009549,0.0020804911323328784,0.007929740791268758,3.410641200545703e-05,3.410641200545703e-05,0.0,0.004280354706684857,6.821282401091406e-05,0.007349931787175989
+0.0,0.42772328342798144,3.971248163297724e-05,0.012668281640919741,0.00015884992653190897,0.0298439299471824,1.985624081648862e-05,3.971248163297724e-05,0.0013899368571542036,0.19326079186688377,0.0,3.971248163297724e-05,0.00013899368571542036,0.00013899368571542036,0.00011913744489893174,0.02454231364917994,0.00230332393471268,3.971248163297724e-05,1.985624081648862e-05,0.0007148246693935904,0.002521742583694055,0.0016480679877685556,1.985624081648862e-05,0.0005956872244946587,7.942496326595449e-05,0.005877447281680632,0.00011913744489893174,0.29593741312894645
+0.0,0.07903953601739935,0.0008749671887304226,0.020399235028686423,0.025884029348899416,0.017196855117933077,0.0011899553766733747,0.0022049173156006648,0.00033248753171756057,0.011642063422621652,9.499643763358873e-05,0.0003999850005624789,0.07594465207554717,0.013214504456082897,0.04049848130695099,0.024181593190255365,0.006899741259702761,5.749784383085634e-05,0.01051710560853968,0.03773608489681637,0.0037698586303013637,0.0075047185730535105,0.028846418259315276,0.00024249090659100284,0.00047998200067497467,6.749746884491831e-05,0.0010774595952651777,0.5897028861417697
+0.0,0.40388373911101183,0.0005723698905517087,0.0004886084431538977,0.0008794951976770158,0.21811480902389993,2.792048246593701e-05,0.00011168192986374804,0.000991177127540764,0.20157192316283226,2.792048246593701e-05,0.00015356265356265356,0.0005584096493187402,0.0017589903953540316,0.0011168192986374804,0.07377987491623855,5.584096493187402e-05,2.792048246593701e-05,0.004844203707840071,6.980120616484253e-05,4.1880723698905516e-05,0.027669198123743577,6.980120616484253e-05,0.0018846325664507483,1.3960241232968505e-05,0.018092472637927185,0.004606879606879607,0.03858610676792495
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
diff --git a/assets/transition_comparisons.csv b/assets/transition_comparisons.csv
index deac652..b2f5fc3 100644
--- a/assets/transition_comparisons.csv
+++ b/assets/transition_comparisons.csv
@@ -1,3 +1,3 @@
-,l2,kl_mf,kl_fm,jsd,permutation_p_value
-names,0.3189041485139616,0.04320097944655348,0.0215380760498496,0.03236952774820154,0.979
-surnames,1.2770018925640299,0.2936188220992242,0.23989460296618093,0.26675671253270256,0.001
+category,l2,kl_mf,kl_fm,jsd,permutation_p_value
+names,0.3189041485139616,0.04320097944655348,0.0215380760498496,0.03236952774820154,0.973
+surnames,1.2770018925640299,0.2936188220992242,0.23989460296618093,0.26675671253270256,0.003
diff --git a/notebooks/names.ipynb b/notebooks/names.ipynb
index 9ee8f2f..146c2bd 100644
--- a/notebooks/names.ipynb
+++ b/notebooks/names.ipynb
@@ -11,13 +11,15 @@
    "id": "initial_id",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:07.646210Z",
-     "start_time": "2025-09-28T14:57:07.643563Z"
+     "end_time": "2025-09-28T21:41:26.227960Z",
+     "start_time": "2025-09-28T21:41:26.224407Z"
     }
    },
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
     "import sys \n",
     "import os\n",
     "from collections import Counter"
@@ -28,8 +30,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:07.915456Z",
-     "start_time": "2025-09-28T14:57:07.719242Z"
+     "end_time": "2025-09-28T21:41:26.471906Z",
+     "start_time": "2025-09-28T21:41:26.234143Z"
     }
    },
    "cell_type": "code",
@@ -41,12 +43,13 @@
     "from core.config.pipeline_config import PipelineConfig\n",
     "\n",
     "from research.statistics.utils import LETTERS\n",
-    "from research.statistics.utils import identified_category_dist\n",
-    "from research.statistics.utils import explode_words_token\n",
+    "from research.statistics.utils import build_category_distribution\n",
+    "from research.statistics.utils import build_words_token\n",
     "from research.statistics.utils import build_transition_probabilities\n",
     "from research.statistics.utils import build_transition_comparisons\n",
-    "from research.statistics.utils import build_letter_frequencies\n",
-    "from research.statistics.plots import plot_transition_matrix"
+    "from research.statistics.utils import build_ngrams_count\n",
+    "from research.statistics.plots import plot_transition_matrix\n",
+    "from research.statistics.plots import plot_letter_frequencies"
    ],
    "id": "584a6fcfcbea71e4",
    "outputs": [],
@@ -57,8 +60,8 @@
    "id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:08.401248Z",
-     "start_time": "2025-09-28T14:57:08.396493Z"
+     "end_time": "2025-09-28T21:41:26.481898Z",
+     "start_time": "2025-09-28T21:41:26.478191Z"
     }
    },
    "source": [
@@ -82,8 +85,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:36.335481Z",
-     "start_time": "2025-09-28T14:57:08.553275Z"
+     "end_time": "2025-09-28T21:41:53.362337Z",
+     "start_time": "2025-09-28T21:41:26.490902Z"
     }
    },
    "cell_type": "code",
@@ -120,17 +123,17 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:43.780783Z",
-     "start_time": "2025-09-28T14:57:43.246962Z"
+     "end_time": "2025-09-28T21:42:00.759309Z",
+     "start_time": "2025-09-28T21:42:00.246322Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_name_categories = identified_category_dist(df)\n",
+    "df_name_categories = build_category_distribution(df)\n",
     "df_name_categories.head(12)\n",
     "\n",
     "# save data\n",
-    "df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\")"
+    "df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\", index=False)"
    ],
    "id": "378147d2abc9ab24",
    "outputs": [],
@@ -145,25 +148,40 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:45.847127Z",
-     "start_time": "2025-09-28T14:57:43.788533Z"
+     "end_time": "2025-09-28T21:42:04.015038Z",
+     "start_time": "2025-09-28T21:42:00.766470Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "labels = ['Simple', 'Compose']\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
+    "\n",
+    "# Pie 1: Simple vs Compose\n",
+    "labels = [\"Simple\", \"Compose\"]\n",
     "values = [\n",
     "    len(df.query(\"identified_category == 'simple'\")),\n",
     "    len(df.query(\"identified_category == 'compose'\"))\n",
     "]\n",
+    "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
+    "axes[0].set_title(\"Distribution by Category\")\n",
     "\n",
-    "plt.figure(figsize=(6, 6))\n",
-    "plt.pie(values, labels=labels, autopct='%1.1f%%')\n",
-    "plt.axis(\"equal\")\n",
+    "# Pie 2: Male vs Female\n",
+    "labels = [\"Male\", \"Female\"]\n",
+    "values = [\n",
+    "    len(df.query(\"sex == 'm'\")),\n",
+    "    len(df.query(\"sex == 'f'\"))\n",
+    "]\n",
+    "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
+    "axes[1].set_title(\"Distribution by Sex\")\n",
     "\n",
-    "# save figures\n",
-    "plt.savefig(\"../assets/identified_category_distribution.png\")\n",
-    "plt.savefig(\"../assets/identified_category_distribution.svg\")\n",
+    "for ax in axes.flat:\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save figures\n",
+    "plt.savefig(\"../assets/distribution_grid.png\")\n",
+    "plt.savefig(\"../assets/distribution_grid.svg\")\n",
     "\n",
     "plt.show()"
    ],
@@ -172,9 +190,9 @@
     {
      "data": {
       "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
+       "<Figure size 1000x1000 with 2 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data",
@@ -194,8 +212,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:46.356962Z",
-     "start_time": "2025-09-28T14:57:45.874356Z"
+     "end_time": "2025-09-28T21:42:04.514885Z",
+     "start_time": "2025-09-28T21:42:04.038820Z"
     }
    },
    "cell_type": "code",
@@ -249,8 +267,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:57:47.131714Z",
-     "start_time": "2025-09-28T14:57:46.364153Z"
+     "end_time": "2025-09-28T21:42:05.285962Z",
+     "start_time": "2025-09-28T21:42:04.523383Z"
     }
    },
    "cell_type": "code",
@@ -262,13 +280,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:58:19.334139Z",
-     "start_time": "2025-09-28T14:57:47.146184Z"
+     "end_time": "2025-09-28T21:42:36.611043Z",
+     "start_time": "2025-09-28T21:42:05.308895Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_names = explode_words_token(df_base, 'identified_name', 'name')\n",
+    "df_names = build_words_token(df_base, 'identified_name', 'name')\n",
     "df_names = df_names[['name', 'province', 'sex']].reset_index(drop=True)\n",
     "df_names.describe().T"
    ],
@@ -344,13 +362,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:58:41.141954Z",
-     "start_time": "2025-09-28T14:58:19.869428Z"
+     "end_time": "2025-09-28T21:42:59.059882Z",
+     "start_time": "2025-09-28T21:42:37.124804Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_surnames = explode_words_token(df_base, 'identified_surname', 'name')\n",
+    "df_surnames = build_words_token(df_base, 'identified_surname', 'name')\n",
     "df_surnames = df_surnames[['name', 'province', 'sex']].reset_index(drop=True)\n",
     "df_surnames.describe().T"
    ],
@@ -423,55 +441,135 @@
    ],
    "execution_count": 10
   },
-  {
-   "cell_type": "markdown",
-   "id": "98ba6e48-a7c4-4aae-8b46-872489c95faf",
-   "metadata": {},
-   "source": "### Transition probabilities"
-  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:04.922560Z",
-     "start_time": "2025-09-28T14:58:41.384503Z"
+     "end_time": "2025-09-28T21:43:01.097806Z",
+     "start_time": "2025-09-28T21:42:59.860815Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "names_transitions = build_transition_probabilities(df_names['name'])\n",
-    "names_transitions_males   = build_transition_probabilities(df_names.query(\"sex == 'm'\")['name'])\n",
-    "names_transitions_females = build_transition_probabilities(df_names.query(\"sex == 'f'\")['name'])\n",
+    "df_names_m = df_names.query(\"sex == 'm'\")\n",
+    "df_names_f = df_names.query(\"sex == 'f'\")\n",
     "\n",
-    "names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\")"
+    "df_surnames_m = df_surnames.query(\"sex == 'm'\")\n",
+    "df_surnames_f = df_surnames.query(\"sex == 'f'\")"
    ],
-   "id": "12a7094d94ad519f",
+   "id": "7980efe654809722",
    "outputs": [],
    "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:17.020745Z",
-     "start_time": "2025-09-28T14:59:04.964198Z"
+     "end_time": "2025-09-28T21:43:06.100459Z",
+     "start_time": "2025-09-28T21:43:01.376200Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "surnames_transitions = build_transition_probabilities(df_surnames['name'])\n",
-    "surnames_transitions_males = build_transition_probabilities(df_surnames.query(\"sex == 'm'\")['name'])\n",
-    "surnames_transitions_females = build_transition_probabilities(df_surnames.query(\"sex == 'f'\")['name'])\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
     "\n",
-    "surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\")"
+    "labels = [\"Only Male\", \"Only Female\", \"Shared\"]\n",
+    "\n",
+    "# Native names\n",
+    "names_m = set(df_names_m[\"name\"].str.lower())\n",
+    "names_f = set(df_names_f[\"name\"].str.lower())\n",
+    "\n",
+    "values = [\n",
+    "    len(names_m - names_f),\n",
+    "    len(names_f - names_m),\n",
+    "    len(names_m & names_f)\n",
+    "]\n",
+    "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
+    "axes[0].set_title(\"Native - Distribution by Gender\")\n",
+    "\n",
+    "# Surnames\n",
+    "surnames_m = set(df_surnames_m[\"name\"].str.lower())\n",
+    "surnames_f = set(df_surnames_f[\"name\"].str.lower())\n",
+    "\n",
+    "values = [\n",
+    "    len(surnames_m - surnames_f),\n",
+    "    len(surnames_f - surnames_m),\n",
+    "    len(surnames_m & surnames_f)\n",
+    "]\n",
+    "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
+    "axes[1].set_title(\"Surnames - Distribution by Gender\")\n",
+    "\n",
+    "# Formatting\n",
+    "for ax in axes.flat:\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save figures\n",
+    "plt.savefig(\"../assets/name_by_gender_distribution_grid.png\")\n",
+    "plt.savefig(\"../assets/name_by_gender_distribution_grid.svg\")\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "9568492106dd2169",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
    ],
-   "id": "684b467b17955d65",
-   "outputs": [],
    "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:21.075645Z",
-     "start_time": "2025-09-28T14:59:17.790264Z"
+     "end_time": "2025-09-28T21:43:30.859924Z",
+     "start_time": "2025-09-28T21:43:07.728723Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "names_transitions = build_transition_probabilities(df_names['name'])\n",
+    "names_transitions_males   = build_transition_probabilities(df_names_m['name'])\n",
+    "names_transitions_females = build_transition_probabilities(df_names_f['name'])\n",
+    "\n",
+    "names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\", index=False)"
+   ],
+   "id": "12a7094d94ad519f",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:43:44.217712Z",
+     "start_time": "2025-09-28T21:43:30.869697Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "surnames_transitions = build_transition_probabilities(df_surnames['name'])\n",
+    "surnames_transitions_males = build_transition_probabilities(df_surnames_m['name'])\n",
+    "surnames_transitions_females = build_transition_probabilities(df_surnames_f['name'])\n",
+    "\n",
+    "surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\", index=False)"
+   ],
+   "id": "684b467b17955d65",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:43:49.373562Z",
+     "start_time": "2025-09-28T21:43:45.960221Z"
     }
    },
    "cell_type": "code",
@@ -524,7 +622,7 @@
      }
     }
    ],
-   "execution_count": 13
+   "execution_count": 15
   },
   {
    "metadata": {},
@@ -535,15 +633,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:22.072443Z",
-     "start_time": "2025-09-28T14:59:21.120458Z"
+     "end_time": "2025-09-28T21:43:50.242298Z",
+     "start_time": "2025-09-28T21:43:49.382953Z"
     }
    },
    "cell_type": "code",
    "source": [
     "names_transitions_diff = names_transitions_males['df_probs'] - names_transitions_females['df_probs']\n",
     "surnames_transitions_diff = surnames_transitions_males['df_probs'] - surnames_transitions_females['df_probs']\n",
-    "\n",
     "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n",
     "\n",
     "hm1 = sns.heatmap(names_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[0], cbar=False)\n",
@@ -553,8 +650,6 @@
     "hm2 = sns.heatmap(surnames_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[1], cbar=False)\n",
     "axes[1].set_title(\"Surnames Transition Difference (Male − Female)\")\n",
     "axes[1].set_xlabel(\"Next letter\"); axes[1].set_ylabel(\"\")\n",
-    "\n",
-    "# Shared colorbar\n",
     "cbar = fig.colorbar(hm2.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
     "cbar.set_label(\"Difference in transition probability (Male − Female)\")\n",
     "\n",
@@ -577,13 +672,13 @@
      }
     }
    ],
-   "execution_count": 14
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.145603Z",
-     "start_time": "2025-09-28T14:59:22.140871Z"
+     "end_time": "2025-09-28T21:43:53.422514Z",
+     "start_time": "2025-09-28T21:43:51.881491Z"
     }
    },
    "cell_type": "code",
@@ -598,6 +693,7 @@
     "        'f': surnames_transitions_females\n",
     "    }\n",
     ")\n",
+    "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\", index_label=\"category\")\n",
     "df_comparisons.head(3)"
    ],
    "id": "dae836cd8a6c26e6",
@@ -606,8 +702,8 @@
      "data": {
       "text/plain": [
        "                l2     kl_mf     kl_fm       jsd  permutation_p_value\n",
-       "names     0.318904  0.043201  0.021538  0.032370                0.979\n",
-       "surnames  1.277002  0.293619  0.239895  0.266757                0.001"
+       "names     0.318904  0.043201  0.021538  0.032370                0.973\n",
+       "surnames  1.277002  0.293619  0.239895  0.266757                0.003"
       ],
       "text/html": [
        "<div>\n",
@@ -642,7 +738,7 @@
        "      <td>0.043201</td>\n",
        "      <td>0.021538</td>\n",
        "      <td>0.032370</td>\n",
-       "      <td>0.979</td>\n",
+       "      <td>0.973</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>surnames</th>\n",
@@ -650,19 +746,19 @@
        "      <td>0.293619</td>\n",
        "      <td>0.239895</td>\n",
        "      <td>0.266757</td>\n",
-       "      <td>0.001</td>\n",
+       "      <td>0.003</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
+   "execution_count": 17
   },
   {
    "metadata": {},
@@ -684,19 +780,6 @@
    ],
    "id": "30078f474d5f456e"
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.290360Z",
-     "start_time": "2025-09-28T14:59:24.286314Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\")",
-   "id": "b5d674fb3a7f1c83",
-   "outputs": [],
-   "execution_count": 16
-  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -706,99 +789,47 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.854294Z",
-     "start_time": "2025-09-28T14:59:24.348456Z"
+     "end_time": "2025-09-28T21:44:08.716709Z",
+     "start_time": "2025-09-28T21:43:53.482784Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "def plot_letter_grid(df_all: pd.DataFrame, use=\"freq\", sort_values=False):\n",
-    "    \"\"\"\n",
-    "    Plot a 1×3 grid of letter distributions for Male, Female, and Both.\n",
-    "    `use` ∈ {\"freq\",\"count\"} controls y-axis.\n",
-    "    \"\"\"\n",
-    "    # Slice datasets (adapt values if your labels are 'M'/'F', etc.)\n",
-    "    df_male   = df_all[df_all['sex'].str.lower() == str('m').lower()]\n",
-    "    df_female = df_all[df_all['sex'].str.lower() == str('f').lower()]\n",
-    "\n",
-    "    L_m = build_letter_frequencies(df_male['name'])\n",
-    "    L_f = build_letter_frequencies(df_female['name'])\n",
-    "\n",
-    "    y = \"freq\" if use == \"freq\" else \"count\"\n",
-    "    if sort_values:\n",
-    "        L_m = L_m.sort_values(y, ascending=False)\n",
-    "        L_f = L_f.sort_values(y, ascending=False)\n",
-    "\n",
-    "    ymax = max(L_m[y].max(), L_f[y].max()) * 1.10\n",
-    "\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(18, 5), sharey=True, constrained_layout=True)\n",
-    "    for ax, data, title in zip(axes, [L_m, L_f], [\"Male\", \"Female\"]):\n",
-    "        ax.bar(data[\"letter\"], data[y], color=\"steelblue\", alpha=0.8)\n",
-    "        ax.set_title(title)\n",
-    "        ax.set_xlabel(\"Letter\")\n",
-    "        ax.set_ylim(0, ymax)\n",
-    "        ax.grid(axis=\"y\", alpha=0.3)\n",
-    "    axes[0].set_ylabel(\"Frequency\" if y == \"freq\" else \"Count\")\n",
-    "    plt.show()"
-   ],
-   "id": "4a0d8e2a74a6406c",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'pd' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot_letter_grid\u001B[39m(df_all: \u001B[43mpd\u001B[49m.DataFrame, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m, sort_values=\u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m      2\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m      3\u001B[39m \u001B[33;03m    Plot a 1×3 grid of letter distributions for Male, Female, and Both.\u001B[39;00m\n\u001B[32m      4\u001B[39m \u001B[33;03m    `use` ∈ {\"freq\",\"count\"} controls y-axis.\u001B[39;00m\n\u001B[32m      5\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m      6\u001B[39m     \u001B[38;5;66;03m# Slice datasets (adapt values if your labels are 'M'/'F', etc.)\u001B[39;00m\n",
-      "\u001B[31mNameError\u001B[39m: name 'pd' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 17
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.856757Z",
-     "start_time": "2025-09-28T14:52:15.817423Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plot_letter_grid(df_names, use=\"freq\")",
+   "source": "plot_letter_frequencies(df_names_m, df_names_f, title=\"names\")",
    "id": "9265e47639c4319d",
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'plot_letter_grid' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[19]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_letter_grid\u001B[49m(df_names, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mNameError\u001B[39m: name 'plot_letter_grid' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ],
+      "image/png": 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f/OY3UyqVMnz48Nx4443NE48vqwcffDAPPvjgYst79uyZ9ddfP9/61rfSp0+f/N//+3/zox/9KKuuumq22GKLfPvb384qq6ySJNl5553zy1/+Mqecckr22WefjB07NgcffHDOP//8HHLIIbn++uszYsSIj+z341JTU5NrrrkmP/jBD3L11VfnzTffTN++fXPQQQe1mCD7sMMOS7du3TJhwoTceOONGT58eI488sj88Ic/bN5myy23zMknn5ybbropv/vd75oDgq985SvN23Tt2jW33HJLLr744nz3u9/NnDlzsvbaa+eEE07IwQcfvFRj/9rXvpauXbvmuuuuy09/+tOsueaaOeyww3LYYYctcft999033bp1y49+9KP89Kc/TdeuXbPJJpvkoosuSv/+/ZMk3/nOd/L9738/EyZMyIwZM9K7d+/ss88+Oe6445ZqbAAAsCSl8gdnJAQAAOBjcfvtt2fMmDH5/e9/v0yX9gIAgJWFOTUAAAAAAIBCEGoAAAAAAACF4PJTAAAAAABAIThTAwAAAAAAKAShBgAAAAAAUAhCDQAAAAAAoBDqqj2AImhqakpDQ0NqampSKpWqPRwAAAAAAGhXyuVympqaUldXl5qaDz8fQ6jRCg0NDXnyySerPQwAAAAAAGjXhgwZko4dO37oeqFGKyxKhYYMGZLa2toqj6b9amxszJNPPtmujrOeikFPxaCnYtBTMeipGNpjT0n77EtPxaCnYtBTMeipGPRUDHoqhvbY04po0XH+d2dpJEKNVll0yana2lpP2o9BezzOeioGPRWDnopBT8Wgp2Jojz0l7bMvPRWDnopBT8Wgp2LQUzHoqRjaY08roo+aAsJE4QAAAAAAQCEINQAAAAAAgEIQagAAAAAAAIVgTg0AAAAAAFY6jY2NWbhwYau2S5J58+aZU2MZdOjQoSLHT6gBAAAAAMBKo1wu57XXXsvMmTNbvX1dXV3+/ve/f+Qk1vx7q622WtZcc81lOo5CDQAAAAAAVhqLAo011lgjXbt2/cgP2Mvlcurr69OlSxehRhuVy+XMnTs3r7/+epJkrbXWanMtoQYAAAAAACuFxsbG5kCjd+/erbpPuVxOU1NTOnfuLNRYBl26dEmSvP7661ljjTXafCkqE4UDAAAAALBSWDSHRteuXas8kpXTouPemrlMPoxQAwAAAACAlYozLqqjEsddqAEAAAAAABSCUAMAAAAAAP6Nap/ZMWjQoAwaNCjTpk1bbN1PfvKTDBo0KJdddlmram2//fa5/fbbKz3Ej42JwgEAAAAAWKnVL2jIgoWNS1xXTlIu12TB3AWpVLTRsUNtunRcuo/nO3TokPvuuy9f//rXWyy/9957qx66fJyEGgAAAAAArNQWLGzMLQ9MyZtz5i1hbTlNTU2pqalJKhBr9F6lc7621SeXOtQYMWLEYqHGO++8k7/97W/5zGc+s8zjKgqhBgAAAAAAK70358zL9Fn1S1hT2VCjrXbYYYeMGzcu77zzTrp3754k+eMf/5gRI0akvv5/x71gwYJcfPHFmThxYt5666307ds3RxxxRL785S8vVrNcLueKK67IT37yk8ybNy8jRozImWeemX79+n1sfS0tc2oAAAAAAMAK7lOf+lT69u2b+++/v3nZPffckx133LHFdtdcc03++Mc/5rLLLstdd92VvfbaK+ecc07eeOONxWrefPPN+fWvf52LL744P/3pT9O7d+8cfPDBWbhw4XLvp62EGgAAAAAAUAA77LBD7rvvviTvnZHx0EMPZYcddmixzac//emcd9552XjjjdO/f/8ceeSRWbhwYV5++eXF6v3oRz/K6NGjs9lmm2X99dfPd77zncyaNSsPPPDAx9FOm7j8FAAAAAAAFMAOO+yQY489Ng0NDfnLX/6ST33qU+ndu3eLbXbcccc89NBDufDCC/Piiy/m6aefTpI0NracCP3dd9/Na6+9luOPP/5/Lq31nnnz5i0xAFlRCDUAAAAAAKAAhg8fniSZNGlS7r333uy0006LbfO9730vt912W0aNGpW99torZ511VrbffvvFtlsUcvzgBz/IwIEDW6xbddVVl8PoK8PlpwAAAAAAoADq6uqyzTbb5L777ssf/vCHxebTSJJbb701Z5xxRk488cTsvvvuzZOIl8vlFtv16NEjvXv3zowZMzJgwIAMGDAga621Vr773e/mpZde+lj6aQuhBgAAAAAAFMQOO+yQ2267Lb17907//v0XW7/aaqvlD3/4Q1555ZU8+uijGT16dJL35uD4oAMPPDDf//73c9999+Xll1/O6aefnsceeyzrrbfecu+jrVx+CgAAAACAlV7vVTp/yJpympqa/mfeidJy3E/rjBw5Mg0NDUs8SyNJzj///IwdOzZ77LFH+vbtm3333Te1tbV55plnsvXWW7fY9pBDDsm7776bM888M++880422mijXHfddSv05aeEGqxQ6uo8JQEAAACAj1fHDrX52lafXOK6ct67dFOpVKpApPG/+1sazz33XPPfu3XrlieeeKLF+ptuuqn578OHD8+vf/3rFusPP/zw5r/fd999zX+vra3N8ccfn+OPP36pxlNNPkFmhbLBgE+kZl59yh+9aevVdUipU6dKVgQAAAAA2pEuHevSpeOSPy4vl8upr69Ply5dUipVKtagrYQarFBqy+U03n93Mnt2ReqVeqyamq13SoQaAAAAAEAbfXCSbaqnqqHG/Pnzc/bZZ+fuu+9O586dc/DBB+fggw/+t/d59NFHc/LJJ+f3v/9987JBgwYtcdtx48Zlr732yj333JNvfvObLdbtsssuufTSS5e9CSquPHtWSrNmVqZWRaoAAAAAALAiqGqoMX78+Dz11FO54YYbMm3atJx88snp169fdt111yVu/9xzz+W4445Lpw986/7BBx9scfvHP/5x7rzzzuywww5JkilTpmS77bbLOeec07zNB2sAAAAAAAArtqqFGnPnzs1tt92Wa6+9NoMHD87gwYPzwgsv5JZbblliqHHrrbdm3Lhx6d+/f955550W6/r06dP891deeSU33XRTrrrqqqyyyipJkqlTp+ZTn/pUi+0AAAAAAIBiqanWjp999tk0NDRk2LBhzcuGDx+eyZMnp6mpabHt77///owbNy4HHnjgv6176aWXZosttsjnP//55mVTp07NuuuuW6mhAwAAAAAAVVC1UGPGjBnp2bNnOnbs2Lxs9dVXz/z58zNz5szFtr/iiiuy8847/9ua06ZNy29+85scddRRzcvK5XJeeumlPPjgg9lll12y44475qKLLsqCBQsq1gsAAAAAALD8Ve3yU/X19S0CjSTNt9saOPz85z/PRhttlKFDhzYvmzZtWvO+vv/97+ef//xnzj333MybNy+nn376UtUvl8tmuV+O3n9sK32Uq/W4Ldpve3ru6KkY9FQMeioGPRWDnoqjPfalp2LQUzHoqRj0VAx6KgY9ffyWdXwrYk9F8u+Of2uPbdVCjU6dOi0WXiy63blz5zbV/N3vfpevfOUrLZatvfbaeeSRR7LqqqumVCplww03TFNTU0466aSMGTMmtbW1ra4/e/bs1NRU7eSWlUKXJE1NTSk3NlakXqmpKU1NTXl3zpwlXtZseVu0z/b03NFTMeipGPRUDHoqBj0VR3vsS0/FoKdi0FMx6KkY9FQMevr4LViwIE1NTWlsbEzjUn4GubTbs7jGxsY0NTVlzpw5mT9/fot1rf38tmqhRt++ffP222+noaEhdXXvDWPGjBnp3LlzevTosdT1Xn311UyZMiU77LDDYutWW221FrfXX3/9zJ8/P7NmzUqvXr1avY8ePXosVQjC0mlsbEzjnP95savUca6pSU1NTVbp2q0y9ZbSohe69vTc0VMx6KkY9FQMeioGPRVHe+xLT8Wgp2LQUzHoqRj0VAx6+vjNmzcvb775Zmpra5dqfKVSqar9bL/99pk2bdpiyzfZZJP83//7fz+2cey3337ZdNNNc8wxx7Tp/rW1te99XrvKKoud3NDa0KhqocaGG26Yurq6PP744xkxYkSSZNKkSRkyZEibErzJkydnrbXWSr9+/Vosf+CBB3LiiSfmj3/8Y7p06ZIkeeaZZ7LaaqstVaCRvPfELZVKSz02Wuf9x7bSR7laj9ui/ban546eikFPxaCnYtBTMeipONpjX3oqBj0Vg56KQU/FoKdi0NPH78PGV54/P2lY+KH361wuJ3PfrdxA6jqk1KlTqzcvlUo59dRTs/vuu7dY3qFDh4/1OC86bm3d5797frS2ZtVCjS5dumSvvfbK2LFjc/755+f111/PhAkTcsEFFyR576yNJaU1H+aFF17I+uuvv9jyYcOGpVOnTjn99NNz9NFH55VXXsn48eNz6KGHVrQfAAAAAAAKqmFhmu6/J+XZsxZbVc578z2USqWKfBm71GPV1Gy9U7IUoUaSrLLKKunTp08FRlBsVb2o2ZgxYzJ48OAccMABOfvss3PMMcdk5513TpKMHDkyEydObHWtN954I6uuuupiy7t3757rrrsub731Vvbee++cdtpp+fKXvyzUAAAAAACgWXn2rGTW20v8U5751oeuW9o/SwpOlmnc5XJ++MMfZuTIkRkxYkSOPPLIFpeqGjRoUO68887stttuGTp0aL797W/nlVdeyf7775+hQ4fmP//zPzN9+vTmWldddVW23377bLTRRhk5cmQuv/zyD933rbfemu233z7Dhg3Lfvvtl+eee66ivS1J1c7USN47W2PcuHEZN27cYus+rPlRo0Zl1KhRiy0/++yzP3Q/G2ywQa6//vq2D5SPz4p3RhoAAAAAwArr5ptvzq9//etcfPHFWX311TNhwoQcfPDB+fWvf50OHTokSS699NJceOGFqa+vz6GHHpq//vWvOe200zJmzJgce+yxufbaa3P66afnF7/4RW644YZccskl6d+/fx544IGMHTs22223XQYPHtxiv/fdd18uv/zynHP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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "execution_count": 19
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.857123Z",
-     "start_time": "2025-09-25T21:39:45.174312Z"
+     "end_time": "2025-09-28T21:44:18.302146Z",
+     "start_time": "2025-09-28T21:44:10.480696Z"
     }
    },
    "cell_type": "code",
-   "source": "plot_letter_grid(df_names, use=\"freq\", sort_values=True)",
+   "source": "plot_letter_frequencies(df_surnames_m, df_surnames_f, title=\"surnames\")",
    "id": "395dace523f1bed4",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Figure size 1800x500 with 2 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ],
-      "image/png": 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D2W677VL65+lhSqVStt122zz44IOZOHFiTj755Gy44YYty5dKpZTL5SxcuDBJ8vTTT2f48OHvUWdAUZUXzE9p/rzK1KpIFYD/M3fh4rw0v6Eitcplr1IAAAAUU9VCjTlz5mSdddZJ9+7dW6atu+66WbJkSebNm5f+/fu3WnazzTZrdf8BAwbkqaeeSpKMHTu21bwbb7wxjY2N2W677ZIsDzWam5uz3377ZeHChRk/fnxOPvnk9OnTZ3W1BwAAAAAAVFjVQo2GhoZWgUaSlttLly5t07JvXS5ZflTH1KlTc/jhh2fgwIFZtmxZnnvuuWy44YY599xzs2DBgpx33nk56aSTctlll7VrzOVy2Tcb36Tc8rvy26TSFau131ast5qPna64n/TUzjFUut4a/HyqtM7QU1d87BWpp6Rtfa2unjyfKjyGlt8ee20eQ6Xr6anCa39z/QrW816iYjpDT0V67euK7yX05P3Rv9IZXiMqTU/FoCc6qq3btmqhRn19/UqhxIrbPXr0aNOyb13uT3/6U4444oiMHz8+xx9/fJKkW7du+cMf/pD6+vp069YtSfL1r389H/vYx/LSSy9l0KBBbR7zggULWp0Wa01WU1OT5lK3NDU3p6mpMhcubW5u/ud/yylXqGapuTnNzc15Y+HClvrvpRXrrNZjpyvuJz21TbV7Wh2q/XxaHardU1d87BWlp6Ttfa2Onpr+ub7XX3+9IvU6A8+ntqvmYy+p7uuEntqu2q/nq0O1XydWh2r3VJTXvq74XkJP3h+1RbVfI1YHPRWDnuiotr5HrFqoMWjQoLz22mtpbGxMXd3yYcyZMyc9evRI3759V1r2lVdeaTXtlVdeyXrrrddy+7777svRRx+dnXfeORdeeGGrB9dbTzO16aabJkm7Q42+ffumtra2zct3dfMXLU1tTU3FtsmKfVZTU0oqtZ1ralJTU5O1evWuTL12WvHmqpqPna64n/TUltVXv6dK6wzPp0rrDD11xcdeIXpaXrDNfVW6p9p/9tSnTx/Ppwry2Gvr6qv7OqGntq6++q/nldYZXicqrTP0VIjXvq74XkJP3h+1QWd4jag0PRWDnuiotgbVVQs1ttxyy9TV1eXBBx9suSbGrFmzMmrUqJXSrtGjR+fKK69MuVxuuQj4Aw88kKOPPjpJ8uc//zmf+9zn8qEPfSgXXXRRS0iSJLNnz84BBxyQn/3sZxkyZEiS5PHHH09dXV2GDh3arjGXSqWWi5WTlFp+V36bVLpitfbbmy9uX7UxtPzuOvtJT+0cQ6XrrcHPp0rrDD11xcdekXpK2tbX6urJ86nCY2j57bHX5jFUup6eKrz2N9evYD3vJSqmM/RUpNe+rvheQk/eH/0rneE1otL0VAx6oqPaum2rdqxMz549s//++2fy5Ml5+OGHM3PmzMyYMSMHH3xwkuVHbSxevDhJsvfee2fBggU555xzMnv27JxzzjlpaGjIPvvskyQ588wzs8EGG2TSpEl57bXXMmfOnJb7b7LJJhk6dGi+8pWv5M9//nPuv//+fOUrX8kBBxyQfv36Vat9AAAAAACgnap6ArBJkyZlxIgROeSQQzJlypQcd9xx2WuvvZIk48aNyy233JJk+aF/l19+eWbNmpWJEyfmoYceyhVXXJFevXplzpw5+dOf/pTZs2dn1113zbhx41p+brnlltTU1OSyyy5Lnz59cuCBB+bYY4/NTjvtlNNOO62arQMAAAAAAO1UtdNPJcuP1pg6dWqmTp260rwnn3yy1e2tt946N99880rLDRw4cKVl32qDDTbItGnT3t1gAQAAAACAqnKpdgAAAAAAoBCEGgAAAAAAQCEINQAAAFgj1NVV9QzMAABUgHd0AAAAdDoNSxuzdFlTxeqVk2w4dFjF6gEAUB1CDQAAADqdpcuacu3dszN34eKK1Ovfpz4H7Di0IrUAAKgeoQYAAACd0tyFi/PS/IaK1CqXyxWpA3RdNaVStYcAQBsINQAAAABYo/XuUZd+3WtSs7ghFY1A67qlVF9fyYoAazyhBgAAAABrtB7d6lJqakzTPTOTBQsqUrPUt19qxu+ZCDUAKkqoAQAAAABJygvmpzR/XmVqVaQKAG9VU+0BAAAAAAAAtIVQA1azujoHRAEAAAAAVIJPW+FNGpY2ZumyporVKyfZcOiwitUDuh7BJwAAAEDb+SQF3mTpsqZce/fszF24uCL1+vepzwE7Dq1ILaD6VkfwOWTjTSpWDwAAAKCrE2rAW8xduDgvzW+oSK1y2WXBoCupdPC5Yf/e+fROG6VmcUNlLyJY1y2l+vpKVgQAAADoFIQaANAOlQw++/epT6mpMU33zEwWLKhIzVLffqkZv2ci1AAAAAC6IKEGAFRZecH8lObPq0ytilQBAAAA6Jxqqj0AAAAAAACAthBqAAAAAAAAhSDUAAAAAAAACkGoAatZTalU7SEAAAAAAHQJLhQOq1HvHnXp170mNYsbKnvx3rpuKdXXV7IiAAAAAECnJ9SA1ahHt7qUmhrTdM/MZMGCitQs9e2XmvF7JkINAAAAAGANI9SA90B5wfyU5s+rTK2KVAEAAAAAKB7X1AAAAAAAAApBqAEAAAAAXVBdnZO0UB0ee6xOHl0AAHQJ/nACAGht86EbpWZxQ2VPZV3XLSXX+exSGpY2ZumyporVKyfZcOiwitWDt/KXHwAA7zl/OAEArH615XKa7ro9WbCgIvVKffulZvyeiVCjS1m6rCnX3j07cxcurki9/n3qc8COQytSC1ZFqAEAsAaqKZWqun5/OAEAvDfKC+anNH9eZWpVpAqd0dyFi/PS/IaK1CqXPVJYvYQaAABrmN496tKve03lT0WQtOt0BP5wAgBYzar7PRbWYNX+EhVdm1ADAGAN06NbXUpNjWm6Z2bFTkWQOB0B0Pn5gAXozCp9es5SqZTetXVprlhFaJvV9iUq13Phn4QaAABrqEqeiiBxOgKgc/MBC9DZVfr0nJsMWisfH7N+RWpBe6yOL1H5AhVvJtQAAACgy/MBC1AElTw9Z/8+XpuoLtdzYXURagBQGHV1/tkCAN4dH7AAABSbT4cAWC0qfT7YcpINhw6rWD0AAAAAikeoAcBqUenzwfbvU58DdhxakVoAAAAAFJNQA4DVppLngy2XneABAAAAYE1XU+0BAAAAAAAAtIVQAwAAAAAAKAShBgAAAAAAUAhCDQAAAAAAoBCEGgAAAAAAQCEINQAojJpSqdpDAAAAAKCK6qo9AABoi9496tKve01qFjekXMnCdd1Sqq+vZEUAAAAAVhOhBgCF0KNbXUpNjWm6Z2ayYEFFapb69kvN+D0ToQYAAABAIQg1ACiU8oL5Kc2fV5laFakCAAAAwHvFNTUAAAAAAIBCEGoAAAAAAACFINQAAKBLqCmVqj0EAAAAVjPX1AAAoPB696hLv+41qVncUPnr5dR1S6m+vtJVAQAA6AChBgAAhdejW11KTY1pumdmsmBBxeqW+vZLzfg9E6EGAABApyDUAACgyygvmJ/S/HmVq1exSgAAAFSCa2oAAAAAAACFINQAAAAAAAAKQagBAAAAAAAUglADAAAAACiEurqud4ngrthTV9SjR49qD4F/quozZsmSJZkyZUpuv/329OjRI4cddlgOO+ywVS772GOP5atf/Wr+/Oc/Z7PNNsuUKVMycuTIlvm/+MUv8q1vfStz5szJuHHjctZZZ6V///5JknK5nAsvvDA//vGP09zcnP/8z//Ml7/85dTUyHQAAAAoLh+wAJ1Zw9LGLF3WVLF65SQbDh1WsXod0RV76ooqvZ9SSoYPG5aaxQ0pV67qcnXdUqqvr3TVLq2qocb555+fRx99NFdffXWef/75nHLKKRk8eHD23nvvVsstWrQoRx55ZPbbb798/etfz3XXXZejjjoqv/rVr9KrV688/PDDOf300zNlypRsscUWOeecczJp0qRcfvnlSZLvfe97+cUvfpFp06alsbExJ510UgYMGJDDDz+8Gm0DAACwBirMByw+XAEqZOmyplx79+zMXbi4IvX696nPx3fauCK1Omp19HTAjkMrUov/U+n9tMmgtfLxMeun6Z6ZyYIFFamZJKW+/VIzfs/Ev7vtUrVQY9GiRbnxxhtz5ZVXZsSIERkxYkSeeuqpXHvttSuFGrfcckvq6+tz8sknp1Qq5fTTT89dd92V2267LRMnTswPf/jD7LPPPtl///2TLA9Ldttttzz33HMZMmRIfvCDH+QLX/hCxo4dmyT58pe/nG9/+9tCDQAAAN4zRfiAxYcrQKXNXbg4L81vqEitXvW16de9puphbiV7Kpcr/r1//qmS+6l/n+WPj/KC+SnNn1eRmkkqf9THGqJqocYTTzyRxsbGjBkzpmXadtttl+nTp6e5ubnVqaEeeuihbLfddimVSkmSUqmUbbfdNg8++GAmTpyYhx56KEcccUTL8htssEEGDx6chx56KN27d88LL7yQD3zgA63W849//CMvv/xy1ltvvfegWwAAAOj8H7D4cAXozHp0q0upqbHLhbk1//zME2ibqoUac+bMyTrrrJPu3bu3TFt33XWzZMmSzJs3r+V6GCuW3WyzzVrdf8CAAXnqqaeSZJXhxIABA/Liiy9mzpw5SdJq/rrrrpskefHFF4UaAAAAAFAgXSnM7d2jrlMcfQJFUrVQo6GhoVWgkaTl9tKlS9u07IrlFi9e/LbzFy9e3Kr2v1rP21lxGFhjY6NDwt6kqakp6/XtnrpSZbZJ/97d0tTUlPRdO+VSbUVqlvr2TZqaksbGNi2vp3emp7bRk57aQk9t0xV7StrXl57emZ7arpo9JdV/ndBT21S7p6QYzyk96akt9NQ2etJTW3TFngav3TPNS5dk2SOzUm6ozJF8pZ69UjN6bFLbtm1kP72zzvDefE3Q1LT82mPv9Bl8qVylT+lvvfXWnH322bn33ntbpj399NPZd999c99992XttddumX7kkUfm/e9/f7785S+3TPvGN76Rp59+OtOnT88222yTiy++OOPHj2+Zf8ABB2TffffNdtttlwMOOCAPP/xw6v+ZTi5evDijR4/OTTfdlBEjRrzjWJcuXZpHHnmkAl0DAAAAAABvZ9SoUSsdxPBmVTtSY9CgQXnttdfS2NiYurrlw5gzZ0569OiRvn37rrTsK6+80mraK6+80nLqqLebP3DgwAwaNKil9oYbbtjy/0kycODANo21rq4uo0aNSk1NTct1PQAAAAAAgMool8tpbm5uyQveTtVCjS233DJ1dXV58MEHM3bs2CTJrFmzWsKDNxs9enSuvPLKlMvllEqllMvlPPDAAzn66KNb5s+aNSsTJ05Mkrzwwgt54YUXMnr06AwaNCiDBw/OrFmzWkKNWbNmZfDgwW2+nkZNTc2/TIYAAAAAAIDVr+adF1k9evbsmf333z+TJ0/Oww8/nJkzZ2bGjBk5+OCDkyw/mmLF9TD23nvvLFiwIOecc05mz56dc845Jw0NDdlnn32SJJ/85Cfz05/+NDfeeGOeeOKJnHzyydl1110zZMiQlvkXXHBB7rvvvtx333258MILW9YDAAAAAAAUQ9WuqZEsvwD45MmTc/vtt6dPnz45/PDDc+ihhyZJhg8fnvPOO6/l6IuHH344X/3qV/P0009n+PDhmTJlSrbaaquWWjfddFMuvvjizJ8/PzvvvHPOOuusrLPOOkmWX2Dk/PPPz0033ZTa2tr853/+Z0488USnkgIAAAAAgAKpaqgBAAAAAADQVlU7/RQAAAAAAEB7CDUAAAAAAIBCEGoAAAAAAACFINSA1eDvf/97hg8fnr///e/VHgrtdMkll+Sggw6q9jBYhV//+tcZP358Ro8enbvvvrvaw+EtuvrrXlfrTz/F0ZV7A6C4Hn/88TzwwAPVHgYAayihBgCFcPHFF2fcuHG55ZZb8oEPfKDaw2ENs8EGG+See+7JBhtsUO2hVERX6wcAeG8de+yx+ctf/lLtYQCwhqqr9gAAoC0WLlyY7bbbLu973/uqPRTWQLW1tRk4cGC1h1ExXa0fAAAA1hyO1KDqZs2alU9+8pMZPXp0ttlmmxxxxBF5+eWXqz2sirjtttsyfvz4bLvttjnzzDOzdOnSag/pXfnrX/+aww8/PGPGjMmuu+6aH/zgB9Ue0rs2e/bslsffwQcfnNdee63aQ3rXXnjhhRx99NEZPXp0JkyYkGnTpqWpqanaw3pXJkyYkH/84x857bTTMmHChGoPpyKee+65HHrooRk9enT222+/XHXVVV2it5kzZ2aPPfbI6NGjc/TRR2f+/PnVHlJFdLVTAHW1ft7qmmuuydixY/P4449Xeyjk/x5vv/3tbzNhwoSMGTMmZ599dv785z9n4sSJ2WabbXLUUUfl9ddfr/ZQ221Fb7fffnv22GOPjBo1KkcddVTmzZtX7aF12Isvvpjjjz8+22+/fXbYYYecffbZhX8Pu2I//fznP8+HPvShjB07NmeffXYaGxurPbQOmz17dj71qU9l9OjR+fSnP53LLrusS5xCdcW+uvTSS/OBD3wgX/va16o9pHftBz/4QXbbbbeMGjUqEydOzP3331/tIb1rBx10UP7xj39k0qRJOfXUU6s9nHdtVe+Lin5a4hNOOCGnnHJKq2knnnhiTj/99CqNqOM++tGP5oc//GHL7c985jP59Kc/3XL7hhtuyCc/+clqDO1du/HGGzNy5Mj89a9/TZI8/fTTGTVqVGbOnFnlkXXcGWeckaOPPrrVtLPOOisnnXRSlUb07l1yySUZPnz4Sj8333xztYe2RhNqUFULFy7MUUcdlZ133jm/+MUvctVVV+Vvf/tbrrjiimoPrSJ+9KMf5Zvf/GamT5+eu+66K5dffnm1h9RhS5YsyWGHHZbevXvnRz/6Uc4888x885vfzG9+85tqD63Dli5dmiOPPDJDhgzJTTfdlA9/+MO54YYbqj2sd6VcLufzn/98BgwYkJtvvjnnnXdefv7zn2f69OnVHtq78uMf/zjrr79+TjvttPz4xz+u9nDetcbGxhx11FHp27dvfvKTn+TII4/MtGnTqj2sirj55ptz0UUX5Qc/+EH+3//7f7nyyiurPSTWMLfddlsuuuiiTJ8+PVtuuWW1h8ObXHHFFfnOd76Ts846K9dcc00+//nP58QTT8xVV12VBx98sNCv79OnT89FF12UH/7wh3nkkUfyve99r9pD6pClS5fmkEMOSUNDQ6655pp861vfym9/+9ucf/751R5aRUybNi3f/OY3M23atNx+++255JJLqj2kDlmyZEmOOOKIvO9978tNN92UPffcs/Dv9d7qgQceyE9+8pMcfPDB1R7Ku/LYY4/l/PPPz1e/+tXceuutGTt2bL74xS+mubm52kN7Vy655JKW9+ZF/JB8TfCRj3wkv/nNb7Js2bIky1/ff/Ob3+QjH/lIlUfWfuPGjcsf//jHJMmyZcvy4IMP5pFHHmnp7d57782HPvShag6xw/7zP/8zY8aMyXnnnZdyuZwzzzwze+21V/bYY49qD63DPvKRj+Tee+9t+bJKc3NzfvnLXxbysbfCYYcdlnvuuafl51Of+lQ22mijQu+nrsDpp6iqxYsX55hjjslnPvOZlEqlDBkyJHvttVcefvjhag+tIk477bRst912SZLjjz8+F1xwQY477rgqj6pj7rnnnrz66qs599xz06dPn2y++eY544wzUlNT3Gz0d7/7XebNm5fJkyenV69e2XTTTfPHP/4xr776arWH1mF/+MMf8vzzz+fGG29MTU1NNtlkk5xyyimZNGlSjj322GoPr8P69++f2trarLXWWunfv3+1h/Ou/eEPf8gLL7yQH/3oR+nTp08222yz/PnPf87//M//VHto79pJJ52UrbfeOkmyzz775IknnqjyiFiT3H///ZkyZUq++c1vZuzYsdUeDm9xzDHHZIsttsgWW2yRc889Nx/5yEey8847J0l22mmnPPPMM1UeYcd94QtfaHnt22+//fLII49UeUQdc/fdd+ell17Kj370o/Tr1y9JcuaZZ+Zzn/tcTjjhhPTu3bvKI3x3TjrppJbXhhXvzb/4xS+mVCpVeWTtc88992T+/PmZMmVKy3vYWbNmdYkjjlc45JBDstFGG1V7GO/aP/7xj5RKpQwePDgbbrhhvvjFL2a33XZLc3Nzof+OWnvttVvem6+11lrVHg6rMH78+DQ3N+e+++7LuHHjcs8996RHjx7ZYYcdqj20dhs3blxOPPHElMvl/L//9/+y0UYb5dVXX81jjz2WUaNG5b777ssRRxxR7WF2SKlUyte+9rX8+7//e7785S/n2WefLWzgvsIOO+yQfv365Y477shHP/rR3H///Vm2bFnLe74i6t27d8t7oDvvvDM33XRTrrvuOq9/VSbUoKoGDhyY/fffP9///vfz+OOPZ/bs2XnyySez7bbbVntoFbHij9sk2WqrrfLKK69k/vz5LX8kFsmzzz6bYcOGpU+fPi3TPvaxj1VxRO/e7Nmzs/HGG6dXr14t00aNGpU777yziqN6d55++unMmzevJUxLln8zYvHixXnttdeyzjrrVHF0rPDkk0+u9HzaZpttukSo8eYPINZaa60sWbKkiqNhTXPmmWemqanJBdA7qSFDhrT8f48ePVpdI6lHjx6FPsXR0KFDW/6/T58+Ld8eLZqnn346G2+8cav3qttuu20aGxvzt7/9rfBHP735b4yRI0fm1VdfzWuvvVa4L0w888wzK72HHTNmTO64444qjqqyuso11MaNG5f3v//92W+//bLVVltl9913zwEHHJC6Oh/FsHp17949e+yxR26//faMGzcut99+ez784Q+ntra22kNrt7Fjx6ahoSFPPfVU/vd//zdjx47Nyy+/nFmzZqW2tjY1NTUZOXJktYfZYcOGDcuRRx6ZSy65JFOnTi3cv0lvVVNTk3322Se33XZbPvrRj+bWW2/NnnvumW7dulV7aO/a3//+95x88smZNOn/b+/+Y6qq/ziOv/ACAkstpYhJ0Y0aZSsrzJlczGlzcDMFM6bp1JZUVLrmUhJBl4CtaCtECUyn5AodE4mmMBAmDuWHlkrmJLtN5OoGzrUCQSChv2CQfr8VYMdzfT62u3E+cMbr7N6dnXven/P+rNa4ceOMjnPbM+/UALiExsZGzZo1S1VVVXrssccUHx+vV1991ehYQ6bv7Jvu7m5JMu2J3FUvvHvelx5mfX96/PHHH3rwwQeVn5/f+yooKFBxcTGzCG4hFovlus/eX7fNysyzDmF+K1as0PTp012iB7sr+uuNFFc6X5j9+qHH8OHDrxvrWZfL7OtzSf3fp572P2Z7SkOSvL29r7tu8PT0NCjNzXGjz6IZeXt7Kzc3V9nZ2Zo4caLy8vI0Z84cNTY2Gh0NfdzoPGDmNXd62O12lZaWqqOjQ2VlZbLb7UZHGhBPT09NmDBBNTU1OnbsmEJCQhQSEqLvv/9elZWVCg0NNeW5vK8zZ87IYrGourra6ChDYubMmaqoqFBLS4tKSkpM3XqqR3t7u5YvX66wsDDNmzfP6DgQRQ0YrKSkRKNGjVJWVpYWL16sCRMmqKGhwWVu7v3000+9P9fW1uree+/tN6PKTB544AHV19erra2td+yjjz5ScnKygakG5+GHH9a5c+fU3NzcO2b2RWWtVqsuXryo0aNHKzAwUIGBgXI6ndq4caPpL/RcSc9nr++iuD/++KOBiQDX8PzzzysuLk6nTp1Sfn6+0XEA07FarTp37ly/hc5PnDghd3d3l2gF1Pc679SpU7rnnntM+RRrUFDQddewp0+fNjAR/pfjx48rKytLkyZN0urVq1VUVKT29nZ99913RkdDHz0FzytXrvSO9V003KwmT56sa9euafv27fLy8jJ1a86edTVOnDjRr6hRUVFh2vU0ehw4cEAVFRXKzMzUt99+q8rKSqMjDdr48ePl5+enL774Qt3d3Zo4caLRkQZt/fr1amtrY/LULYSiBgx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      },
      "metadata": {},
      "output_type": "display_data",
@@ -807,158 +838,428 @@
      }
     }
    ],
-   "execution_count": 209
+   "execution_count": 19
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### N-grams analysis",
+   "id": "d3dc057ce0869f93"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Prefixes and Suffixes",
+   "id": "67994bde86f2f6fc"
   },
   {
-   "cell_type": "code",
-   "id": "98c7ed61-5c2d-4d9a-9d85-e4efe4bfc402",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.857443Z",
-     "start_time": "2025-09-25T21:35:00.831997Z"
+     "end_time": "2025-09-28T21:44:19.998062Z",
+     "start_time": "2025-09-28T21:44:19.989365Z"
     }
    },
+   "cell_type": "code",
    "source": [
-    "def count_ngrams(df: pd.DataFrame, n: int, col: str = \"name\") -> pd.DataFrame:\n",
-    "    # Normalize and clean\n",
-    "    _names = (\n",
-    "        df[col]\n",
-    "        .astype(str)\n",
-    "        .str.lower()\n",
-    "        .str.replace(r\"[^a-z]\", \"\", regex=True)\n",
-    "    )\n",
+    "import re\n",
+    "import pandas as pd\n",
     "\n",
-    "    # Collect n-grams from all names\n",
-    "    ngrams = []\n",
-    "    for name in _names:\n",
-    "        if len(name) >= n:\n",
-    "            ngrams.extend(name[i:i+n] for i in range(len(name) - n + 1))\n",
+    "def _pick_ngram_col(df: pd.DataFrame) -> str:\n",
+    "    \"\"\"Detect the n-gram column name like '2-gram', '3-gram', etc.\"\"\"\n",
+    "    for c in df.columns:\n",
+    "        if isinstance(c, str) and c.endswith(\"-gram\"):\n",
+    "            return c\n",
+    "    raise ValueError(f\"Could not find an n-gram column in: {list(df.columns)}\")\n",
     "\n",
-    "    # Count\n",
-    "    counter = Counter(ngrams)\n",
-    "\n",
-    "    # Build DataFrame\n",
-    "    df_ngrams = (\n",
-    "        pd.DataFrame(counter.items(), columns=[f\"{n}-gram\", \"count\"])\n",
-    "        .sort_values(\"count\", ascending=False)\n",
-    "        .reset_index(drop=True)\n",
-    "    )\n",
-    "    df_ngrams[\"freq\"] = df_ngrams[\"count\"] / df_ngrams[\"count\"].sum()\n",
-    "    return df_ngrams\n",
-    "\n",
-    "def plot_ngrams_grid(df: pd.DataFrame, n: int, top_k: int = 20,\n",
-    "                     gender_col=\"sex\", male_value=\"m\", female_value=\"f\", name_col=\"name\"):\n",
+    "def build_ngram_summary_table(results: dict, top_k: int = 20, sep: str = \", \") -> pd.DataFrame:\n",
     "    \"\"\"\n",
-    "    Plot top n-grams for Male, Female, and All in a 1×3 grid.\n",
+    "    Convert dict of {subset_key: df} into rows of:\n",
+    "      sex | 2-grams | 3-grams | 4-grams | position\n",
+    "\n",
+    "    Expected subset_key pattern: '{sex}_{position}_{n}', e.g. 'male_prefix_3', 'female_suffix_4', 'male_any_2'\n",
     "    \"\"\"\n",
-    "    # Split datasets\n",
-    "    df_male   = df[df[gender_col].str.lower() == str(male_value).lower()]\n",
-    "    df_female = df[df[gender_col].str.lower() == str(female_value).lower()]\n",
+    "    rows = {}\n",
     "\n",
-    "    # Compute n-grams\n",
-    "    ng_male   = count_ngrams(df_male, n, col=name_col)\n",
-    "    ng_female = count_ngrams(df_female, n, col=name_col)\n",
+    "    for key, df in results.items():\n",
+    "        # Parse key -> sex, position, n\n",
+    "        m = re.match(r\"^(male|female)_(prefix|suffix|any)_(\\d+)$\", key)\n",
+    "        if not m:\n",
+    "            # Skip keys that don't match the expected pattern\n",
+    "            continue\n",
+    "        sex, position, n_str = m.groups()\n",
+    "        n = int(n_str)\n",
     "\n",
-    "    # Plot in a grid\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(18, 6), sharey=True, constrained_layout=True)\n",
-    "    for ax, data, title in zip(\n",
-    "            axes, [ng_male, ng_female], [\"Male\", \"Female\"]\n",
-    "    ):\n",
-    "        sns.barplot(\n",
-    "            data=data.head(top_k),\n",
-    "            x=f\"{n}-gram\", y=\"count\",\n",
-    "            ax=ax\n",
+    "        if df is None or df.empty:\n",
+    "            ng_list_str = \"\"\n",
+    "        else:\n",
+    "            ng_col = _pick_ngram_col(df)\n",
+    "            # take top_k, cast to string, join\n",
+    "            ng_list_str = sep.join(df[ng_col].astype(str).head(top_k).tolist())\n",
+    "\n",
+    "        # group rows by (sex, position)\n",
+    "        rows.setdefault((sex, position), {}).update({f\"{n}-grams\": ng_list_str})\n",
+    "\n",
+    "    # Build final table, ensuring all n columns are present\n",
+    "    records = []\n",
+    "    for (sex, position), cols in rows.items():\n",
+    "        record = {\n",
+    "            \"sex\": sex,\n",
+    "            \"position\": position,\n",
+    "            \"2-grams\": cols.get(\"2-grams\", \"\"),\n",
+    "            \"3-grams\": cols.get(\"3-grams\", \"\"),\n",
+    "            \"4-grams\": cols.get(\"4-grams\", \"\"),\n",
+    "        }\n",
+    "        records.append(record)\n",
+    "\n",
+    "    # Order: sex (male,female), then position (prefix, suffix, any)\n",
+    "    order_pos = {\"prefix\": 0, \"suffix\": 1, \"any\": 2}\n",
+    "    df_out = pd.DataFrame(records)\n",
+    "    if not df_out.empty:\n",
+    "        df_out[\"__pos_order\"] = df_out[\"position\"].map(order_pos).fillna(99)\n",
+    "        df_out = (\n",
+    "            df_out.sort_values([\"sex\", \"__pos_order\"])\n",
+    "            .drop(columns=\"__pos_order\")\n",
+    "            .reset_index(drop=True)\n",
     "        )\n",
-    "        ax.set_title(f\"{title} – Top {top_k} {n}-grams\")\n",
-    "        ax.set_xlabel(f\"{n}-gram\")\n",
-    "        ax.set_ylabel(\"Count\")\n",
-    "        ax.tick_params(axis=\"x\", rotation=45)\n",
-    "\n",
-    "    plt.show()"
+    "    return df_out"
    ],
+   "id": "a685260c6ab8213d",
    "outputs": [],
-   "execution_count": 202
-  },
-  {
-   "cell_type": "code",
-   "id": "412883b2-c1fd-483f-b61a-ba2bfc8b04e7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.857737Z",
-     "start_time": "2025-09-25T21:35:03.778209Z"
-    }
-   },
-   "source": "plot_ngrams_grid(df_names, n=3, top_k=20, gender_col=\"sex\")",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1800x600 with 2 Axes>"
-      ],
-      "image/png": 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7AACoJmuvvXYuvfTSBbbPL39ef/31TJ48OUcffXSTOdI222yTVVZZJX//+9+LZWLy8fX85mvbtm2Spu+z56+MMnPmzCRL9x4++biQGzduXI488sgUCoVipg022CCbbrpp/v73v5csExsaGpqcRVlKXV3dUr+/Hzt2bObOnbvASjk9e/bMeuutl6effrqYp1mzZk3u17Jly+y4447529/+tkTHqq+vz5FHHpn6+vpcd911xQJxUV/Xknw98+bNy6WXXpo5c+bkiiuuyMEHH5zf//732WKLLZr8mycfzwGfeuqpFAqF7LHHHk3G9t577wXKxDZt2hRfS8nSzdM33XTTYpGYfPx6WmONNYpF4vz9z5+ztW3bNptttlnOOOOMPP7449lhhx2y4447ZuDAgYt9DgBYMspEAKrGnnvumf79+2fy5Mlp0aJFnnzyyfzsZz8red+77rorF198cd555520adMmHTt2TMuWLRe67+nTp6dQKDSZAH9SfX39AgXWHXfcsdjJyXrrrZe//OUvi/7CSpgxY0aTidJ88yfl8yfgyceTsk+aX5At7sy5xsbGDB06NNdee2323nvvXHDBBcUJ56qrrpokC3yadf4ZiaU+/fppm2++eZKP/7Nh3rx5GT58eH7+859n9OjRGTFiRJP7vvzyy5k2bVox+yfN/5o/qXXr1k1uT5s2LWeeeWYeeuih1NTUZMMNNyx+2vXTE+xS2T9ZYH7aXnvtlcbGxtx8883F5ZfWW2+9nHjiicWzWAEAoNrMv0TBwkyfPj1JMnjw4AwePHiB8fr6+ia3l/Z99tK8h08+ngM1NjbmyiuvzJVXXrnAeIsWLUoeZ7fddsukSZMWmiNJzjvvvOy7776LvM+nzZ9vlZqvtG3btlh0zb/96ZVu1lprreJzvCgvv/xyjjnmmHzwwQe56qqrmhS0q6yySqZOnbrAYz744IPinG5RVlpppWy//fZJPp637bLLLrn++utz3nnnLXDWav/+/YvXWPz0vK3UPG7+qjaftKTz9FKvpU/PAT+ppqYm11xzTS699NI8+OCDufPOO7PSSitl1113zeDBg5sUkwB8NspEAKrGjjvumJVXXjn33XdfWrdunfXXXz9bbbXVAvd75plncsopp+SQQw7JkUceWSzbhg4dWvL6e8nH5Vnr1q1zww03lByff63AT9p5553zhz/8YZGZP3lB+6Wx+uqrZ8qUKQtsn79t/tlzSfKf//ynyX3mTybXXHPNhe5/7ty5+cUvfpEHHnggRxxxRE4++eQmn1z96le/mrq6ugWuL/Hmm28myULPeJw0aVKeeOKJfPe7320ymZ8/Ea2vr8+BBx7Y5BPM833lK18pORF+7733sskmmyz0a0mSE088Ma+//nquu+66dO/ePc2bN8/s2bNz2223LfJxS2rvvffO3nvvnf/+9795/PHHc+WVV+akk05Kjx49FihzAQDgy2C11VZLkpx88snp1avXAuOft6BZ2vfwK6+8cmpqanL44Yfn29/+9gLjCysuL7300sUudbn++usvdf75X//UqVMXmK9MmTKlyYdDS5WGU6dOLVnCfdI//vGP9OvXL6uuumpuuumm4rUS59t4443z+OOPp7GxscnqMW+88cYiV6n5y1/+klVXXTXbbLNNcduqq66aDTbYoFgSf3qu265duzz55JPF7Ouuu25xbNq0aYv8OpLPNk9fGu3bt89ZZ52VM888M+PHj899992XK6+8MmusscYC12QEYOkpEwGoGs2bN8+uu+6a+++/Py1btiw5gUyS5557Lo2NjRkwYEDx05gNDQ154oknkmSBiVaS9OrVK9dcc00KhUK6dOlS3D569Og8+OCDOffccxc4zhprrNGk1FuWttlmm9xwww2ZNGlSk2U+77rrrqy99tpNys2//OUv6du3b/H2/fffn5qammy33XYL3f/AgQPz4IMPZuDAgcWlTT+pRYsW6dmzZx588MEceeSRxaLx/vvvz6qrrtrkOfqkt99+O6effnpatWrVZBmfv//971lppZWy8cYbZ/XVVy9ZwG2zzTZ57LHHMmfOnGIR+dJLL+Wtt95qMsktZcyYMfnBD36Qbbfdtrht/pJBi1uydXF+9rOf5aOPPsrIkSOz6qqrZs8998xKK62Ufv36pb6+XpkIAMCX0iabbJK11lorb731Vo488sji9vr6+px88sn54Q9/mK9+9aufef9L+x5+lVVWyZZbbpnXX3+9yRmVH374YY4//vj06dOneCmIT+rQocNnzrgoXbt2TfPmzXPPPfc0KVufeeaZvP3228VlXOdnfOyxx9K7d+/i7b/97W/F26W89NJLOeaYY7L++uvn6quvLjnv2GGHHXLZZZflscceK14rcNq0aXnmmWdy9NFHL3Tf1113Xd57773cddddxctyTJ48Oa+99lpxadZSZ6326NEjdXV1efDBB3PYYYcVtz/wwAMLPdZ8n2WevqSee+659OvXL5dddlm6dOmSjh07pmPHjnn00Ufz9ttvf6Z9AtCUMhGAqrLXXnvl6KOPTm1tbU4//fSS95lfdJ199tnZb7/9MmPGjNx0000ZP358kmTWrFkLLJvSp0+fbLPNNjnuuONy3HHHZdNNN83zzz+fYcOGpXfv3os8y295+PGPf5y77rorhx9+ePr37582bdrkzjvvzD/+8Y+ce+65TSZZY8eOzYknnpjvfe97GT9+fIYPH54DDzyw5DKpSfLQQw/lnnvuyS677JJu3bpl7NixTca33HLLNG/ePMcee2x+/OMf56c//Wn222+/PPfcc7n66qvzi1/8YqGf+u3Ro0e+8Y1v5Fe/+lXef//9fPWrX80jjzySm266KQMGDFjkp5ePOeaY3HvvvTnqqKNyxBFHZObMmbnkkktSW1u72Ot9dOnSJXfffXc6deqUr3zlK3n22WdzxRVXpKamJrNnz17kYxdnu+22y5lnnpkLLrggO+64Y2bOnJkRI0Zko402yhZbbPG59g0AAJWqrq4uP//5zzNo0KDU1dVl5513zsyZMzNq1Ki8++67CyyDubQ+y3v4E044IX379s0vfvGLfPe7301DQ0OuueaajBs3Lscdd9znyrO02rRpk759+2bkyJFZaaWVsvPOO+ett97KJZdcks022yzf//73m9x/4MCB+dnPfpa11lorV199dWbNmpVjjz12ofs/7bTTMm/evAwYMCDvvPNO3nnnneLYmmuuma9+9avZZptt0qtXr5x00kk56aST0qZNmwwfPjyrrrpqfvSjHy1038cdd1yOOOKI/PznP8+BBx6YadOmZdSoUVlttdVyxBFHLPRxG2ywQfbbb79cfPHF+eijj7LFFlvkwQcfzCOPPJIkiywDP8s8fUltueWWadmyZU4++eQMGDAgbdu2zRNPPJH//d//zaGHHvqZ9glAU8pEAKrKN77xjay22mpZZ511Frpsy7bbbptBgwbl2muvzX333Ze2bdtm2223zYgRI9KvX7+MGTOm+KnN+Wpra3PFFVfkkksuyeWXX5733nsv7du3z49//OP069fvi/jSmlh77bXz+9//Pr/+9a8zZMiQ4kRt1KhR+eY3v9nkvocddljefffd9O/fP2ussUaOOeaYRX4Kdf6nRv/yl7+UvJ7jww8/nPXXXz9f//rXM3z48AwbNiz9+vVL+/btc/LJJy9ycllbW5vhw4dn5MiRueKKK1JfX5+NNtooZ599dg444IBFfs0bbrhhrr766gwdOjTHH3981lprrRx99NG59NJLS15v45POP//8/OpXv8qvfvWrJMlGG22UwYMH56677sozzzyzyMcuzg9/+MN89NFHueWWW3LzzTenZcuW+frXv56TTjopK6200ufaNwAAVLIDDjggK6+8cq666qrceuutad26dbbeeutcdNFFC/3w4pL6LO/hd9hhh1x99dUZMWJEjj/++Ky00krp1KlTrr322nTr1u1z5fks5hdXN954Y2699da0adMme+yxR372s58tcI2/s846K+eee26mTZuWrbfeOr///e9LXk4jSSZOnJiXXnopSXL88ccvMP79738/559/fpJkxIgROf/88zN06NA0NjZm6623zm9/+9tFfpBzu+22yzXXXJNhw4bl+OOPT7NmzdK7d++ceOKJJa8B+UlnnHFGWrdunWuuuSbvv/9+vv71r+fYY4/NyJEjF3ldw88yT19SLVq0yDXXXJNf//rXOeecczJz5sziPHRpr4UJQGk1hVJXNAYAqkKHDh3Sv3//DBgwoNxRPrcnn3wyK620Unr27FncNnPmzHzjG9/IySef7BOlAABA1Rk+fHhGjBiRl19+udxRPrfp06cXl2f95CU/Lrjggvzxj3/MU089VcZ0ACxPzkwEACrCv/71rwwbNiwnnHBCOnXqlOnTp+faa6/Nqquu2uT6iwAAAHzxWrVqlXPOOScdO3bMYYcdltatW2fs2LG58cYbF7k6DgDVT5kIAFSEI444InPnzs3vf//7vPPOO2ndunV69eqV88477wu/ZiUAAABNtWjRItddd11++9vf5tRTT83s2bPz1a9+NaecckoOPvjgcscDYDmyzCkAAAAAAABQUm25AwAAAAAAAACVSZkIAAAAAAAAlKRMBAAAAAAAAEpqVu4AX0aNjY2ZN29eamtrU1NTU+44AAAAFa1QKKSxsTHNmjVLbe1n/8yruRgAAMCSW9K5mDJxOZg3b15eeOGFcscAAACoKp07d07z5s0/8+PNxQAAAJbe4uZiysTlYH5727lz59TV1ZU5DQAAQGVraGjICy+88LnOSkzMxQAAAJbGks7FlInLwfzldOrq6kxgAQAAltDnXZrUXAwAAGDpLW4u9vk+9gkAAAAAAAB8aSkTAQAAAAAAgJKUiQAAAAAAAEBJykQAAAAAAACgJGUiAAAAAAAAUJIyEQAAAAAAAChJmQgAAAAAAACUpEwEAAAAAAAASlImAgAAAAAAACUpEwEAAAAAAICSlIkAAAA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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 203
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.858071Z",
-     "start_time": "2025-09-25T21:35:45.428602Z"
+     "end_time": "2025-09-28T21:46:12.618810Z",
+     "start_time": "2025-09-28T21:44:20.007216Z"
     }
    },
    "cell_type": "code",
-   "source": "plot_ngrams_grid(df_names, n=4, top_k=20, gender_col=\"sex\")",
-   "id": "b4eda839e6cc5d8f",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1800x600 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
+   "source": [
+    "names_ngrams = {\n",
+    "    # male\n",
+    "    \"male_prefix_2\": build_ngrams_count(df_names_m, 2, \"prefix\"),\n",
+    "    \"male_prefix_3\": build_ngrams_count(df_names_m, 3, \"prefix\"),\n",
+    "    \"male_prefix_4\": build_ngrams_count(df_names_m, 4, \"prefix\"),\n",
+    "    \"male_suffix_2\": build_ngrams_count(df_names_m, 2, \"suffix\"),\n",
+    "    \"male_suffix_3\": build_ngrams_count(df_names_m, 3, \"suffix\"),\n",
+    "    \"male_suffix_4\": build_ngrams_count(df_names_m, 4, \"suffix\"),\n",
+    "    \"male_any_2\": build_ngrams_count(df_names_m, 2, \"any\"),\n",
+    "    \"male_any_3\": build_ngrams_count(df_names_m, 3, \"any\"),\n",
+    "    \"male_any_4\": build_ngrams_count(df_names_m, 4, \"any\"),\n",
+    "\n",
+    "    # female\n",
+    "    \"female_prefix_2\": build_ngrams_count(df_names_f, 2, \"prefix\"),\n",
+    "    \"female_prefix_3\": build_ngrams_count(df_names_f, 3, \"prefix\"),\n",
+    "    \"female_prefix_4\": build_ngrams_count(df_names_f, 4, \"prefix\"),\n",
+    "    \"female_suffix_2\": build_ngrams_count(df_names_f, 2, \"suffix\"),\n",
+    "    \"female_suffix_3\": build_ngrams_count(df_names_f, 3, \"suffix\"),\n",
+    "    \"female_suffix_4\": build_ngrams_count(df_names_f, 4, \"suffix\"),\n",
+    "    \"female_any_2\": build_ngrams_count(df_names_f, 2, \"any\"),\n",
+    "    \"female_any_3\": build_ngrams_count(df_names_f, 3, \"any\"),\n",
+    "    \"female_any_4\": build_ngrams_count(df_names_f, 4, \"any\"),\n",
+    "}"
    ],
-   "execution_count": 204
+   "id": "3924bde83845366f",
+   "outputs": [],
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.858335Z",
-     "start_time": "2025-09-25T21:36:21.247703Z"
+     "end_time": "2025-09-28T21:46:14.363513Z",
+     "start_time": "2025-09-28T21:46:14.334119Z"
     }
    },
    "cell_type": "code",
-   "source": "plot_ngrams_grid(df_names, n=5, top_k=20, gender_col=\"sex\")",
-   "id": "55605ab05b9a10bb",
+   "source": [
+    "names_ngrams_summary = build_ngram_summary_table(names_ngrams, top_k=10)\n",
+    "\n",
+    "names_ngrams_summary.to_csv(\"../assets/names_ngrams_summary.csv\", index=False)\n",
+    "names_ngrams_summary"
+   ],
+   "id": "9ceaae3cc9040c31",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Figure size 1800x600 with 2 Axes>"
+       "      sex position                                 2-grams  \\\n",
+       "0  female   prefix  ka, ma, mu, mb, ng, ba, ki, lu, ts, bo   \n",
+       "1  female   suffix  ba, ga, la, ka, ma, da, go, ya, bo, na   \n",
+       "2  female      any  ng, ka, mb, an, ba, ma, nd, ga, la, am   \n",
+       "3    male   prefix  ka, mu, ma, ba, mb, ng, ki, lu, ts, bo   \n",
+       "4    male   suffix  ba, ga, la, go, ka, da, bo, le, di, ma   \n",
+       "5    male      any  ng, ka, mb, ba, an, ma, mu, nd, am, al   \n",
+       "\n",
+       "                                            3-grams  \\\n",
+       "0  tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man   \n",
+       "1  nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu   \n",
+       "2  nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng   \n",
+       "3  kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu   \n",
+       "4  nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele   \n",
+       "5  nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo   \n",
+       "\n",
+       "                                             4-grams  \n",
+       "0  tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...  \n",
+       "1  anga, amba, ongo, umba, inga, ombo, unga, enga...  \n",
+       "2  anga, amba, ongo, tshi, umba, inga, ombo, unga...  \n",
+       "3  tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...  \n",
+       "4  amba, ongo, anga, umba, unga, ombo, anda, enga...  \n",
+       "5  amba, ongo, anga, tshi, umba, unga, ombo, anda...  "
       ],
-      "image/png": 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"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>position</th>\n",
+       "      <th>2-grams</th>\n",
+       "      <th>3-grams</th>\n",
+       "      <th>4-grams</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>prefix</td>\n",
+       "      <td>ka, ma, mu, mb, ng, ba, ki, lu, ts, bo</td>\n",
+       "      <td>tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man</td>\n",
+       "      <td>tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>suffix</td>\n",
+       "      <td>ba, ga, la, ka, ma, da, go, ya, bo, na</td>\n",
+       "      <td>nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu</td>\n",
+       "      <td>anga, amba, ongo, umba, inga, ombo, unga, enga...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>any</td>\n",
+       "      <td>ng, ka, mb, an, ba, ma, nd, ga, la, am</td>\n",
+       "      <td>nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng</td>\n",
+       "      <td>anga, amba, ongo, tshi, umba, inga, ombo, unga...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>prefix</td>\n",
+       "      <td>ka, mu, ma, ba, mb, ng, ki, lu, ts, bo</td>\n",
+       "      <td>kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu</td>\n",
+       "      <td>tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>suffix</td>\n",
+       "      <td>ba, ga, la, go, ka, da, bo, le, di, ma</td>\n",
+       "      <td>nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele</td>\n",
+       "      <td>amba, ongo, anga, umba, unga, ombo, anda, enga...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>male</td>\n",
+       "      <td>any</td>\n",
+       "      <td>ng, ka, mb, ba, an, ma, mu, nd, am, al</td>\n",
+       "      <td>nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo</td>\n",
+       "      <td>amba, ongo, anga, tshi, umba, unga, ombo, anda...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
+     "execution_count": 22,
      "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": 205
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:47:17.247916Z",
+     "start_time": "2025-09-28T21:46:14.408394Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "surnames_ngrams = {\n",
+    "    # male\n",
+    "    \"male_prefix_2\": build_ngrams_count(df_surnames_m, 2, \"prefix\"),\n",
+    "    \"male_prefix_3\": build_ngrams_count(df_surnames_m, 3, \"prefix\"),\n",
+    "    \"male_prefix_4\": build_ngrams_count(df_surnames_m, 4, \"prefix\"),\n",
+    "    \"male_suffix_2\": build_ngrams_count(df_surnames_m, 2, \"suffix\"),\n",
+    "    \"male_suffix_3\": build_ngrams_count(df_surnames_m, 3, \"suffix\"),\n",
+    "    \"male_suffix_4\": build_ngrams_count(df_surnames_m, 4, \"suffix\"),\n",
+    "    \"male_any_2\": build_ngrams_count(df_surnames_m, 2, \"any\"),\n",
+    "    \"male_any_3\": build_ngrams_count(df_surnames_m, 3, \"any\"),\n",
+    "    \"male_any_4\": build_ngrams_count(df_surnames_m, 4, \"any\"),\n",
+    "\n",
+    "    # female\n",
+    "    \"female_prefix_2\": build_ngrams_count(df_surnames_f, 2, \"prefix\"),\n",
+    "    \"female_prefix_3\": build_ngrams_count(df_surnames_f, 3, \"prefix\"),\n",
+    "    \"female_prefix_4\": build_ngrams_count(df_surnames_f, 4, \"prefix\"),\n",
+    "    \"female_suffix_2\": build_ngrams_count(df_surnames_f, 2, \"suffix\"),\n",
+    "    \"female_suffix_3\": build_ngrams_count(df_surnames_f, 3, \"suffix\"),\n",
+    "    \"female_suffix_4\": build_ngrams_count(df_surnames_f, 4, \"suffix\"),\n",
+    "    \"female_any_2\": build_ngrams_count(df_surnames_f, 2, \"any\"),\n",
+    "    \"female_any_3\": build_ngrams_count(df_surnames_f, 3, \"any\"),\n",
+    "    \"female_any_4\": build_ngrams_count(df_surnames_f, 4, \"any\"),\n",
+    "}"
+   ],
+   "id": "951e8301584e549c",
+   "outputs": [],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:47:19.856774Z",
+     "start_time": "2025-09-28T21:47:19.831696Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "surnames_ngrams_summary = build_ngram_summary_table(surnames_ngrams, top_k=10)\n",
+    "\n",
+    "surnames_ngrams_summary.to_csv(\"../assets/surnames_ngrams_summary.csv\", index=False)\n",
+    "surnames_ngrams_summary"
+   ],
+   "id": "f7ee40432896ef54",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      sex position                                 2-grams  \\\n",
+       "0  female   prefix  ma, ch, be, na, an, sa, es, jo, ju, me   \n",
+       "1  female   suffix  ne, ie, te, le, ce, ia, se, el, ah, th   \n",
+       "2  female      any  ne, in, el, an, ie, ri, ra, li, ar, er   \n",
+       "3    male   prefix  jo, je, pa, ch, ma, da, al, ju, be, fr   \n",
+       "4    male   suffix  in, el, an, ck, on, re, ce, er, se, is   \n",
+       "5    male      any  an, er, el, ie, in, ri, is, on, en, re   \n",
+       "\n",
+       "                                            3-grams  \\\n",
+       "0  mar, cha, est, chr, gra, dor, sar, rut, ben, mer   \n",
+       "1  ine, tte, lle, rah, nce, ene, nne, her, lie, rie   \n",
+       "2  ine, tte, ett, mar, ari, lle, lin, eli, the, ell   \n",
+       "3  jea, jos, chr, pat, mar, jon, fra, dan, cha, ben   \n",
+       "4  ean, tin, ick, ier, ard, uel, ert, ain, iel, ise   \n",
+       "5  ric, sti, jea, ean, tin, ris, ier, ist, ick, jos   \n",
+       "\n",
+       "                                             4-grams  \n",
+       "0  mari, chri, esth, grac, sara, dorc, ruth, rach...  \n",
+       "1  ette, line, tine, elle, ther, arie, ille, rcas...  \n",
+       "2  ette, line, ther, mari, tine, elle, rist, chri...  \n",
+       "3  jean, chri, jose, jona, patr, fran, mich, emma...  \n",
+       "4  jean, stin, rick, bert, seph, than, oise, avid...  \n",
+       "5  jean, stin, rist, rick, chri, hris, usti, bert...  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>position</th>\n",
+       "      <th>2-grams</th>\n",
+       "      <th>3-grams</th>\n",
+       "      <th>4-grams</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>prefix</td>\n",
+       "      <td>ma, ch, be, na, an, sa, es, jo, ju, me</td>\n",
+       "      <td>mar, cha, est, chr, gra, dor, sar, rut, ben, mer</td>\n",
+       "      <td>mari, chri, esth, grac, sara, dorc, ruth, rach...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>suffix</td>\n",
+       "      <td>ne, ie, te, le, ce, ia, se, el, ah, th</td>\n",
+       "      <td>ine, tte, lle, rah, nce, ene, nne, her, lie, rie</td>\n",
+       "      <td>ette, line, tine, elle, ther, arie, ille, rcas...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>any</td>\n",
+       "      <td>ne, in, el, an, ie, ri, ra, li, ar, er</td>\n",
+       "      <td>ine, tte, ett, mar, ari, lle, lin, eli, the, ell</td>\n",
+       "      <td>ette, line, ther, mari, tine, elle, rist, chri...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>prefix</td>\n",
+       "      <td>jo, je, pa, ch, ma, da, al, ju, be, fr</td>\n",
+       "      <td>jea, jos, chr, pat, mar, jon, fra, dan, cha, ben</td>\n",
+       "      <td>jean, chri, jose, jona, patr, fran, mich, emma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>suffix</td>\n",
+       "      <td>in, el, an, ck, on, re, ce, er, se, is</td>\n",
+       "      <td>ean, tin, ick, ier, ard, uel, ert, ain, iel, ise</td>\n",
+       "      <td>jean, stin, rick, bert, seph, than, oise, avid...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>male</td>\n",
+       "      <td>any</td>\n",
+       "      <td>an, er, el, ie, in, ri, is, on, en, re</td>\n",
+       "      <td>ric, sti, jea, ean, tin, ris, ier, ist, ick, jos</td>\n",
+       "      <td>jean, stin, rist, rick, chri, hris, usti, bert...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 24
   },
   {
    "cell_type": "markdown",
@@ -973,8 +1274,8 @@
    "id": "28fe77f9-f9b0-4267-9dbb-3969e04b83fb",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.858635Z",
-     "start_time": "2025-09-25T21:43:42.798968Z"
+     "end_time": "2025-09-28T21:47:19.912431Z",
+     "start_time": "2025-09-28T21:47:19.901973Z"
     }
    },
    "source": [
@@ -1056,52 +1357,39 @@
     "    return \"\".join(out).capitalize()"
    ],
    "outputs": [],
-   "execution_count": 210
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.858969Z",
-     "start_time": "2025-09-25T21:43:57.635944Z"
+     "end_time": "2025-09-28T21:48:59.032408Z",
+     "start_time": "2025-09-28T21:48:59.020841Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "# Example preparation and usage\n",
-    "P = prepare_prob_matrix(transitions_male['df_probs'], tokens)\n",
+    "P = prepare_prob_matrix(names_transitions_males['df_probs'], tokens)\n",
     "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
     "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])"
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
    ],
    "id": "a01eb547985d5a62",
-   "outputs": [],
-   "execution_count": 212
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.859246Z",
-     "start_time": "2025-09-25T21:44:03.336042Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "names",
-   "id": "c5bcbff0204ceaa4",
    "outputs": [
     {
      "data": {
       "text/plain": [
        "        name\n",
-       "0    Tolemba\n",
-       "1    Kalanga\n",
-       "2      Mbayo\n",
-       "3     Kukasa\n",
-       "4     Kadamu\n",
-       "5  Bongalula\n",
-       "6      Kanga\n",
-       "7   Mangonga\n",
-       "8   Kayalela\n",
-       "9      Mbalo"
+       "0      Monga\n",
+       "1  Ngonganga\n",
+       "2      Kanda\n",
+       "3  Mbombonda\n",
+       "4     Mbanga\n",
+       "5     Mbanda\n",
+       "6      Manga\n",
+       "7      Bambi\n",
+       "8      Mbale\n",
+       "9      Mamba"
       ],
       "text/html": [
        "<div>\n",
@@ -1128,99 +1416,87 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Tolemba</td>\n",
+       "      <td>Monga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Kalanga</td>\n",
+       "      <td>Ngonganga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Mbayo</td>\n",
+       "      <td>Kanda</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Kukasa</td>\n",
+       "      <td>Mbombonda</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Kadamu</td>\n",
+       "      <td>Mbanga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>Bongalula</td>\n",
+       "      <td>Mbanda</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>Kanga</td>\n",
+       "      <td>Manga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>Mangonga</td>\n",
+       "      <td>Bambi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>Kayalela</td>\n",
+       "      <td>Mbale</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Mbalo</td>\n",
+       "      <td>Mamba</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 213
+   "execution_count": 33
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.859772Z",
-     "start_time": "2025-09-25T21:44:29.403639Z"
+     "end_time": "2025-09-28T21:48:52.059908Z",
+     "start_time": "2025-09-28T21:48:52.049436Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "P = prepare_prob_matrix(transitions_female['df_probs'], tokens)\n",
+    "P = prepare_prob_matrix(names_transitions_females['df_probs'], tokens)\n",
     "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
     "\n",
-    "names = pd.DataFrame(generated_var, columns=[\"name\"])"
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
    ],
    "id": "5d5ec37da189b7a8",
-   "outputs": [],
-   "execution_count": 216
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-28T14:59:24.860061Z",
-     "start_time": "2025-09-25T21:44:33.284314Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "names",
-   "id": "f9da3dad4317ac10",
    "outputs": [
     {
      "data": {
       "text/plain": [
        "          name\n",
-       "0        Ngone\n",
-       "1        Kandi\n",
-       "2     Kangonga\n",
-       "3  Dangangandi\n",
-       "4       Kukale\n",
-       "5        Mando\n",
-       "6       Kasaba\n",
-       "7        Mambo\n",
-       "8     Mpolanga\n",
-       "9      Kangena"
+       "0        Mamba\n",
+       "1        Kanga\n",
+       "2  Mbokangango\n",
+       "3      Belango\n",
+       "4       Mbanga\n",
+       "5        Mungo\n",
+       "6        Langa\n",
+       "7        Kambo\n",
+       "8        Konga\n",
+       "9        Mbulu"
       ],
       "text/html": [
        "<div>\n",
@@ -1247,55 +1523,270 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Ngone</td>\n",
+       "      <td>Mamba</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Kandi</td>\n",
+       "      <td>Kanga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Kangonga</td>\n",
+       "      <td>Mbokangango</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Dangangandi</td>\n",
+       "      <td>Belango</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Kukale</td>\n",
+       "      <td>Mbanga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>Mando</td>\n",
+       "      <td>Mungo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>Kasaba</td>\n",
+       "      <td>Langa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>Mambo</td>\n",
+       "      <td>Kambo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>Mpolanga</td>\n",
+       "      <td>Konga</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Kangena</td>\n",
+       "      <td>Mbulu</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 217
+   "execution_count": 32
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:49:58.339225Z",
+     "start_time": "2025-09-28T21:49:58.329362Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "P = prepare_prob_matrix(surnames_transitions_females['df_probs'], tokens)\n",
+    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ],
+   "id": "746028258a8d8dfe",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       name\n",
+       "0    Jomane\n",
+       "1  Maielane\n",
+       "2     Miela\n",
+       "3   Canella\n",
+       "4    Thelie\n",
+       "5     Siele\n",
+       "6   Mielice\n",
+       "7     Juege\n",
+       "8    Anella\n",
+       "9    Sesane"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jomane</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Maielane</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Miela</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Canella</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Thelie</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Siele</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Mielice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Juege</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Anella</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sesane</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-28T21:49:30.676439Z",
+     "start_time": "2025-09-28T21:49:30.664938Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "P = prepare_prob_matrix(surnames_transitions_males['df_probs'], tokens)\n",
+    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in\n",
+    "                 range(10)]\n",
+    "\n",
+    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
+    "names"
+   ],
+   "id": "7227ac855ee3688f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "           name\n",
+       "0         Jaman\n",
+       "1       Chistis\n",
+       "2       Mannane\n",
+       "3       Celerie\n",
+       "4        Jeusus\n",
+       "5        Drinie\n",
+       "6        Jerien\n",
+       "7  Seristinelel\n",
+       "8   Distugueran\n",
+       "9       Alanche"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jaman</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Chistis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mannane</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Celerie</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Jeusus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Drinie</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Jerien</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Seristinelel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Distugueran</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Alanche</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 34
   },
   {
    "metadata": {},
@@ -1303,7 +1794,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "c0524e989ea1c7b9"
+   "id": "a96630f0d2a9195b"
   }
  ],
  "metadata": {
diff --git a/research/statistics/plots.py b/research/statistics/plots.py
index b62c02d..ff35298 100644
--- a/research/statistics/plots.py
+++ b/research/statistics/plots.py
@@ -1,6 +1,9 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
 import seaborn as sns
 
-from research.statistics.utils import LETTERS
+from research.statistics.utils import LETTERS, build_letter_frequencies
 
 
 def plot_transition_matrix(ax, df_probs, title=""):
@@ -12,4 +15,36 @@ def plot_transition_matrix(ax, df_probs, title=""):
         ax=ax
     )
     ax.set_title(title, fontsize=12)
-    return hm
\ No newline at end of file
+    return hm
+
+
+def plot_letter_frequencies(males, females, sort_values=False, title=None):
+    # Compute frequencies
+    L_m = build_letter_frequencies(males['name']).set_index("letter")["freq"]
+    L_f = build_letter_frequencies(females['name']).set_index("letter")["freq"]
+
+    # Combine into one DataFrame
+    df_plot = pd.DataFrame({"Male": L_m, "Female": L_f}).fillna(0).reset_index()
+    df_plot.to_csv(f"../assets/{title}_letter_frequencies.csv", index=False)
+
+    # Optional sorting
+    if sort_values:
+        df_plot = df_plot.sort_values("Male", ascending=False)
+
+    # Plot side-by-side bars
+    x = np.arange(len(df_plot))
+    w = 0.4
+    fig, ax = plt.subplots(figsize=(16, 6))
+    ax.bar(x - w/2, df_plot["Male"], width=w, label="Male", color="steelblue", alpha=0.8)
+    ax.bar(x + w/2, df_plot["Female"], width=w, label="Female", color="salmon", alpha=0.8)
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(df_plot["letter"])
+    ax.set_ylabel("Frequency")
+    ax.set_xlabel("Letter")
+    ax.set_title(f"{title} - Letter Frequencies")
+    ax.legend()
+    ax.grid(axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    plt.show()
diff --git a/research/statistics/utils.py b/research/statistics/utils.py
index 10ceea8..901fffd 100644
--- a/research/statistics/utils.py
+++ b/research/statistics/utils.py
@@ -23,7 +23,7 @@ def normalize_letters(s):
     return s
 
 
-def identified_category_dist(df: pd.DataFrame) -> pd.DataFrame:
+def build_category_distribution(df: pd.DataFrame) -> pd.DataFrame:
     return (
         df.groupby("province")["identified_category"]
         .value_counts(normalize=True)  # get proportions
@@ -31,7 +31,7 @@ def identified_category_dist(df: pd.DataFrame) -> pd.DataFrame:
     )
 
 
-def explode_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFrame:
+def build_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFrame:
     # Normalize + split once (vectorized)
     s = df[source].fillna('').astype(str)
     s = (
@@ -55,12 +55,26 @@ def explode_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFr
 
 
 def build_letter_frequencies(series: pd.Series) -> pd.DataFrame:
-    s = series.astype(str).str.lower().str.replace(r'[^a-z]', '', regex=True).str.cat(sep='')
-    out = (
-        s.value_counts(normalize=False)
-        .reindex(list(LETTERS), fill_value=0)
-        .rename_axis("letter").reset_index(name="count")
+    # Normalize: lowercase, remove non-letters, concatenate all into one string
+    s = (
+        series.astype(str)
+        .str.lower()
+        .str.replace(r'[^a-z]', '', regex=True)
+        .str.cat(sep='')
     )
+
+    # Convert string into Series of characters
+    chars = pd.Series(list(s))
+
+    # Count letters and ensure all letters are present
+    out = (
+        chars.value_counts(normalize=False)
+        .reindex(list(LETTERS), fill_value=0)
+        .rename_axis("letter")
+        .reset_index(name="count")
+    )
+
+    # Relative frequency
     total = out["count"].sum()
     out["freq"] = out["count"] / (total if total > 0 else 1)
     return out
@@ -209,3 +223,48 @@ def build_transition_comparisons(names_transitions: Dict[str, Any], surnames_tra
     }, index=["names", "surnames"])
 
     return out
+
+import pandas as pd
+from collections import Counter
+from typing import Literal
+
+
+def build_ngrams_count(
+        df: pd.DataFrame,
+        n: int,
+        where: Literal["any", "prefix", "suffix"] = "any",
+) -> pd.DataFrame:
+    # Normalize and clean to a–z
+    names = (
+        df["name"].astype(str)
+        .str.lower()
+        .str.replace(r"[^a-z]", "", regex=True)
+    )
+
+    ngrams = []
+    if where == "any":
+        for s in names:
+            L = len(s)
+            if L >= n:
+                ngrams.extend(s[i:i+n] for i in range(L - n + 1))
+    elif where == "prefix":
+        for s in names:
+            if len(s) >= n:
+                ngrams.append(s[:n])
+    elif where == "suffix":
+        for s in names:
+            if len(s) >= n:
+                ngrams.append(s[-n:])
+    else:
+        raise ValueError("where must be one of: 'any', 'prefix', 'suffix'")
+
+    counter = Counter(ngrams)
+
+    out = (
+        pd.DataFrame(counter.items(), columns=[f"{n}-gram", "count"])
+        .sort_values("count", ascending=False, kind="mergesort")
+        .reset_index(drop=True)
+    )
+    total = out["count"].sum()
+    out["freq"] = out["count"] / (total if total > 0 else 1)
+    return out