{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sumaríssims en femení\n", "\n", "Anàlisi de dades per a [Innovation and Human Rights](https://ihr.world/ca/projecte-sumarissims).\n", "\n", "La preparació de les dades es pot veure [en aquest notebook](https://github.com/martinvirtel/sumarissims-dades/blob/master/work/preparaci%C3%B3%20de%20dades.ipynb).\n", "\n", "La base de dades de la llista de reparació jurídica de víctimes del franquisme inclou dades des de 1937.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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codicognoms_nomcognomsnomgenereedatmun_naixped_naixcom_naixprov_naix...penaindultafuselladesref_arxiudescrcorrecciopena_catcat_naixcat_resaf_cat
17135218ABAD ALFONSO, ErundinaABAD ALFONSOErundinaDona43.0Alcoi--AlcoiàAlacant...SobreseïmentNaNNaN37729.0ANC 2017 07 12NaN07 sobebno exec
1825335ABAD ARBÓS, RicardoABAD ARBÓSRicardoHome30.0Barcelona--BarcelonèsBarcelona...Sense declaració de responsabilitatsNaNNaN12842.0ANC 2017 07 12NaN08 libbbno exec
19123192ABAD BARAS, JoséABAD BARASJoséHome30.0Benavarri--RibagorçaOsca...AbsoltNaNNaN49476.0ANC 2017 07 12NaN08 libecno exec
20171ABAD BATLLONE, JuanABAD BATLLONEJuanHome42.0Barcelona--BarcelonèsBarcelona...Sense declaració de responsabilitatsNaNNaN4063.0ANC 2017 07 12NaN08 libbbno exec
2124485ABAD BOIRA, RicardoABAD BOIRARicardoHome48.0Tauste----Saragossa...Sense declaració de responsabilitatsNaNNaN12915.0ANC 2017 07 12NaN08 libebno exec
\n", "

5 rows × 33 columns

\n", "
" ], "text/plain": [ " codi cognoms_nom cognoms nom genere edat \\\n", "17 135218 ABAD ALFONSO, Erundina ABAD ALFONSO Erundina Dona 43.0 \n", "18 25335 ABAD ARBÓS, Ricardo ABAD ARBÓS Ricardo Home 30.0 \n", "19 123192 ABAD BARAS, José ABAD BARAS José Home 30.0 \n", "20 171 ABAD BATLLONE, Juan ABAD BATLLONE Juan Home 42.0 \n", "21 24485 ABAD BOIRA, Ricardo ABAD BOIRA Ricardo Home 48.0 \n", "\n", " mun_naix ped_naix com_naix prov_naix ... \\\n", "17 Alcoi -- Alcoià Alacant ... \n", "18 Barcelona -- Barcelonès Barcelona ... \n", "19 Benavarri -- Ribagorça Osca ... \n", "20 Barcelona -- Barcelonès Barcelona ... \n", "21 Tauste -- -- Saragossa ... \n", "\n", " pena indult afusellades ref_arxiu \\\n", "17 Sobreseïment NaN NaN 37729.0 \n", "18 Sense declaració de responsabilitats NaN NaN 12842.0 \n", "19 Absolt NaN NaN 49476.0 \n", "20 Sense declaració de responsabilitats NaN NaN 4063.0 \n", "21 Sense declaració de responsabilitats NaN NaN 12915.0 \n", "\n", " descr correccio pena_cat cat_naix cat_res af_cat \n", "17 ANC 2017 07 12 NaN 07 sob e b no exec \n", "18 ANC 2017 07 12 NaN 08 lib b b no exec \n", "19 ANC 2017 07 12 NaN 08 lib e c no exec \n", "20 ANC 2017 07 12 NaN 08 lib b b no exec \n", "21 ANC 2017 07 12 NaN 08 lib e b no exec \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import altair as alt\n", "\n", "\n", "dades = pd.read_msgpack(\"data/processat.msg\")\n", "\n", "dades.query(\"genere != '--'\")[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pena de mort i execucions\n", "\n", "L’Autoritat Militar va ordenar dur a terme 3362 execucions entre 1939 i 1975, de les quals la gran majoria (un total de 2892 o el 86%) va tenir lloc l’any 1939. Un total de 3345 homes i 41 dones van ser condemnades a mort." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genereDonaHomesum
af_cat
exec1733453362
no exec2410311055
sum4143764417
\n", "
" ], "text/plain": [ "genere Dona Home sum\n", "af_cat \n", "exec 17 3345 3362\n", "no exec 24 1031 1055\n", "sum 41 4376 4417" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pena_mort = dades.query(\"pena_cat == '00 mort'\").\\\n", " groupby([\"genere\",\"af_cat\"])[\"pena_cat\"].count().\\\n", " unstack(\"genere\")\n", " \n", "pena_mort_sum=pena_mort.sum(axis=0)\n", "pena_mort_sum.name=\"sum\"\n", "pena_mort = pena_mort.append(pena_mort_sum)\n", "pena_mort[\"sum\"] = pena_mort.sum(axis=1)\n", "\n", "pena_mort" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genereDonaHome
af_catexecno execexecno exec
any_inicial
1937-12-3111
1938-12-31410343
1939-12-3116182876732
1940-12-311120298
1941-12-314541
1942-12-3113132
1943-12-311919
1944-12-311210
1945-12-311720
1946-12-31510
1947-12-3154
1948-12-3132
1949-12-31107
1950-12-313
1951-12-3131
1953-12-311
1954-12-311
1955-12-312
1956-12-313
1960-12-311
1967-12-312
1972-12-311
1973-12-311
1975-12-311
\n", "
" ], "text/plain": [ "genere Dona Home \n", "af_cat exec no exec exec no exec\n", "any_inicial \n", "1937-12-31 1 1\n", "1938-12-31 4 103 43\n", "1939-12-31 16 18 2876 732\n", "1940-12-31 1 1 202 98\n", "1941-12-31 45 41\n", "1942-12-31 1 31 32\n", "1943-12-31 19 19\n", "1944-12-31 12 10\n", "1945-12-31 17 20\n", "1946-12-31 5 10\n", "1947-12-31 5 4\n", "1948-12-31 3 2\n", "1949-12-31 10 7\n", "1950-12-31 3 \n", "1951-12-31 3 1\n", "1953-12-31 1\n", "1954-12-31 1 \n", "1955-12-31 2\n", "1956-12-31 3\n", "1960-12-31 1\n", "1967-12-31 2\n", "1972-12-31 1 \n", "1973-12-31 1 \n", "1975-12-31 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pena_de_mort_anys = dades.query(\"pena_cat == '00 mort'\").\\\n", " groupby([\"af_cat\",\"genere\",pd.Grouper(key=\"any_inicial\",freq=\"1y\")])[\"any_inicial\"].\\\n", " count().\\\n", " rename(columns={ 'genere' : 'penes de mort' }).\\\n", " unstack(\"genere\").\\\n", " unstack(\"af_cat\").\\\n", " fillna(\" \") \n", "\n", "pena_de_mort_anys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Noms únics\n", "\n", "Els 5502 registres presents a la base de dades representen a 5319 noms únics. Una dona, Carmen López Cano,\n", "va ser registrada 3 vegades en 1939, i 181 dones van ser registrades dues vegades." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5502" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dades.query(\"genere == 'Dona'\"))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
3 proc.1
2 proc.181
1 proc.5137
sum5319
\n", "
" ], "text/plain": [ " 0\n", "3 proc. 1\n", "2 proc. 181\n", "1 proc. 5137\n", "sum 5319" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unicos = dades.query(\"genere == 'Dona'\").groupby(\"cognoms_nom\").\\\n", " aggregate({ \"nom\" : \"count\", \n", " \"any_inicial\": lambda a: [b.year for b in a],\n", " \"any_resol\": lambda a: [b.year for b in a]\n", " }).\\\n", " rename(columns={ \"nom\": \"procediments\" }).\\\n", " sort_values(\"procediments\",ascending=False)\n", " \n", "pd.Series({ '3 proc.' : len(unicos.query(\"procediments == 3\")),\n", " '2 proc.' : len(unicos.query(\"procediments == 2\")),\n", " '1 proc.' : len(unicos.query(\"procediments == 1\")),\n", " 'sum' : len(unicos)\n", " }).sort_values().to_frame()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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procedimentsany_inicialany_resol
cognoms_nom
LÓPEZ CANO, Carmen3[1939, 1939, 1939][1939, 1942, 1939]
SABINA JOSEFA, Joaquina2[1939, 1939][1939, 1939]
MANONELLAS SOLÉ, Adelina2[1939, 1942][1939, 1943]
VILA MARQUILLA, Palmira2[1939, 1941][1943, 1943]
VILA GRILLO, Pilar2[1939, 1939][1939, 1942]
MANZANO MARTÍNEZ, María2[1939, 1939][1939, 1939]
GIL GONZÁLEZ, Inés2[1940, 1939][1941, 1941]
ROURA PUIG, Gertrudis2[1939, 1939][1939, 1940]
PUJOL MOR, Magdalena2[1939, 1939][1940, nan]
HIDALGO CORZO, Juana2[1939, 1939][1939, 1939]
LAFARGA VERNÍS, Rosalia2[1940, 1939][1943, 1939]
ESBERT CAPELL, Josefa2[1939, 1939][1939, 1943]
RIVERA MASCARELL, Concepción2[1939, 1939][1939, 1939]
CALVO NAVARRO, Rosalia2[1943, 1939][1944, 1939]
AUSEJO MARTIN, Pilar2[1939, 1939][1941, 1939]
BASCUÑANA GARCIA, Carmen2[1939, 1939][1939, 1939]
SAU ALSINA, Antonia2[1939, 1942][1943, 1943]
GILART NAVARRA, Josefa2[1939, 1940][1939, nan]
GIMENO FERRER, Bienvenida2[1959, 1956][1960, 1959]
AUDOUI DUSTON, Juana2[1940, 1939][nan, 1943]
\n", "
" ], "text/plain": [ " procediments any_inicial \\\n", "cognoms_nom \n", "LÓPEZ CANO, Carmen 3 [1939, 1939, 1939] \n", "SABINA JOSEFA, Joaquina 2 [1939, 1939] \n", "MANONELLAS SOLÉ, Adelina 2 [1939, 1942] \n", "VILA MARQUILLA, Palmira 2 [1939, 1941] \n", "VILA GRILLO, Pilar 2 [1939, 1939] \n", "MANZANO MARTÍNEZ, María 2 [1939, 1939] \n", "GIL GONZÁLEZ, Inés 2 [1940, 1939] \n", "ROURA PUIG, Gertrudis 2 [1939, 1939] \n", "PUJOL MOR, Magdalena 2 [1939, 1939] \n", "HIDALGO CORZO, Juana 2 [1939, 1939] \n", "LAFARGA VERNÍS, Rosalia 2 [1940, 1939] \n", "ESBERT CAPELL, Josefa 2 [1939, 1939] \n", "RIVERA MASCARELL, Concepción 2 [1939, 1939] \n", "CALVO NAVARRO, Rosalia 2 [1943, 1939] \n", "AUSEJO MARTIN, Pilar 2 [1939, 1939] \n", "BASCUÑANA GARCIA, Carmen 2 [1939, 1939] \n", "SAU ALSINA, Antonia 2 [1939, 1942] \n", "GILART NAVARRA, Josefa 2 [1939, 1940] \n", "GIMENO FERRER, Bienvenida 2 [1959, 1956] \n", "AUDOUI DUSTON, Juana 2 [1940, 1939] \n", "\n", " any_resol \n", "cognoms_nom \n", "LÓPEZ CANO, Carmen [1939, 1942, 1939] \n", "SABINA JOSEFA, Joaquina [1939, 1939] \n", "MANONELLAS SOLÉ, Adelina [1939, 1943] \n", "VILA MARQUILLA, Palmira [1943, 1943] \n", "VILA GRILLO, Pilar [1939, 1942] \n", "MANZANO MARTÍNEZ, María [1939, 1939] \n", "GIL GONZÁLEZ, Inés [1941, 1941] \n", "ROURA PUIG, Gertrudis [1939, 1940] \n", "PUJOL MOR, Magdalena [1940, nan] \n", "HIDALGO CORZO, Juana [1939, 1939] \n", "LAFARGA VERNÍS, Rosalia [1943, 1939] \n", "ESBERT CAPELL, Josefa [1939, 1943] \n", "RIVERA MASCARELL, Concepción [1939, 1939] \n", "CALVO NAVARRO, Rosalia [1944, 1939] \n", "AUSEJO MARTIN, Pilar [1941, 1939] \n", "BASCUÑANA GARCIA, Carmen [1939, 1939] \n", "SAU ALSINA, Antonia [1943, 1943] \n", "GILART NAVARRA, Josefa [1939, nan] \n", "GIMENO FERRER, Bienvenida [1960, 1959] \n", "AUDOUI DUSTON, Juana [nan, 1943] " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unicos.query(\"procediments >1\")[:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dones represaliades\n", "\n", "Tres de cada quatre dones encausades ho van ser l’any 1939 (hi corresponen 4109 dels 5502 registres)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Donesperc
any == 193941090.746819
any != 193913930.253181
\n", "
" ], "text/plain": [ " Dones perc\n", "any == 1939 4109 0.746819\n", "any != 1939 1393 0.253181" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "any_1939 = pd.Series({\n", " \"any == 1939\" : dades.query(\"genere == 'Dona' and any_inicial=='1939-12-31'\")[\"pena\"].count(),\n", " \"any != 1939\" : dades.query(\"genere == 'Dona' and any_inicial!='1939-12-31'\")[\"pena\"].count(),\n", " },\n", " name = 'Dones'\n", " ).to_frame()\n", "\n", "\n", "## Nombres únicos, suma\n", "any_1939[\"perc\"]=any_1939.div(any_1939.sum(axis=0),axis=1)\n", "\n", "any_1939" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Les més joves\n", "\n", "L’Autoritat Militar va encausar nenes de 14 i 15 anys. Les condemnades més joves, amb penes de presó, van ser María Angustias Mateos Fernandez i Encarnación Cano Cano, ambdues de 16 anys. María Angustias, l’any 1973 i Encarnación el 1939. María Angustias va ser una de les deu persones a qui es va obrir un procediment sumaríssim aquell any.\n", "\n", "Entre 1939 1975, 87 menors de 18 anys i 466 dones entre 18 y 21 anys van a ser encausades.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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cognoms_nomedatany_inicialany_resolpenaindult
53291RIAU ORTIZ, Antonia1319381938Llibertatnan
15125CITOLER DELGADO, Maria141938nanSobreseïmentnan
764AIXENDRÍ BARBERÀ, Francisca1419401940Sobreseïmentnan
20294ESCRIBANO GIL, Juana1419391939Sense declaració de responsabilitatsnan
64147TOBELLA MOLINA, Juanita1419391939Llibertatnan
44967OLLÉ SOLÉ, Natividad1419441945Sense declaració de responsabilitatsnan
6286BATISTA MOTOS, Enriqueta141939nanSobreseïmentnan
25615GALVIS PAIA, Teresa1519391939Llibertatnan
53839RIERA REIXACH, Josefa151943nanSobreseïmentnan
62530SOLER RIBAS, Rosa1519391939Sobreseïmentnan
62531SOLER RIBAS, Rosa1519391939Pena de multa, remissió o a disposició d'altres autoritatsnan
31068HERNÁNDEZ JUSTRIBÓ, Mercedes1519391940Sobreseïmentnan
42061MONTOLIU TORRUELLA, Ángela1519391939Llibertatnan
63950TENA RIPOLLÈS, Francisca161939nanSobreseïmentnan
28640GÓMEZ ESPINOSA, María de los Dolores161939nanSense declaració de responsabilitatsnan
27050GASCÓN MARTÍNEZ, Aurora1619391941Sense declaració de responsabilitatsnan
763AIXENDRÍ BARBERÀ, Francisca1619431943Arxiunan
25124FUSTÉ GINÉ, Maria1619381938Absoltnan
53808RIERA LLOVET, Margarita1619391942Sobreseïmentnan
23705FLUVIÀ COMPTE, Josefa1619391939Absolt i dos mesos d'arrest majornan
43793NAVARRO MARTÍNEZ, Antonia1619391943Sobreseïmentnan
54233RIVAS AMAT, Isabel1619391943Absoltnan
55771ROMERO SENTÍS, Julia1619391939Absoltnan
21546FALCÓN RODA, Pilar1619391939Sense declaració de responsabilitatsnan
56716RUBINAT FARGUES, Rosa1619381939Sobreseïmentnan
63975TERES ALZURIA, María del Pilar1619381938Sobreseïmentnan
41926MONTE MIR, María Jesús161938nanSobreseïmentnan
57109RULL ALQUÉZAR, María1619401942Sense declaració de responsabilitatsnan
59866SARDÀ VILELLA, Dolores1619391941Sobreseïmentnan
18362DALMAU BALAÑÀ, Dolores1619391940Sobreseïmentnan
50621PRATS ARESTÉ, Teresa1619391940Absoltnan
14462CAZORLA PEDRERO, Carmen1619391939Sobreseïmentnan
62457SOLER JUBERO, Patrocinio1619381939Absoltnan
11671CANO CANO, Encarnación1619391943Deu anys de presó majorIndultada
11306CAMPS PUJOLÀ, Mercedes1619391940Sobreseïmentnan
2348AMOR ALONSO, Amelia1619441945Sobreseïmentnan
4001ASENSIO DÍAZ, Dolores1619401942Sense declaració de responsabilitatsnan
39456MATEOS FERNÁNDEZ, María Angustias161973nanCinc anys de presó menornan
56717RUBINAT FARGUES, Rosa161938nanDesglossament en un altre procedimentnan
49079PINTANELL VILA, Juana1719391939Sense declaració de responsabilitatsnan
47852PERELLÓ SERRA, Carmen1719391939Llibertatnan
44964OLLÉ SOLÉ, Concepción1719441945Sense declaració de responsabilitatsnan
675AGUSTÍ FREIXAS, Rosario1719391939Llibertatnan
53126REVENGA JUÁREZ, Consuelo1719401940Absoltnan
56646ROYO PERPIÑÀ, Conchita1719401944Sense declaració de responsabilitatsnan
60161SEBASTIÀ SABATÉ, Mercedes171939nanSobreseïmentnan
63303SUÑÉ PRAT, Montserrat1719401940Absoltnan
64235TOMÀS CANAL, Josefa1719391939Llibertatnan
66596VALLVÉ VENTURA, Concepción1719391939Absoltnan
66745VÁZQUEZ AGULLÓ, Conchita1719391940Absoltnan
66761VÁZQUEZ LÓPEZ, Isabel1719391939Dos mesos d'arrest governatiunan
52103QUINTANA CINTAS, Amparo1719391939Llibertatnan
42051MONTOLÀ TORTOSA, Joaquina1719401942Sense declaració de responsabilitatsnan
33366LAFUENTE RIGAL, María1719391939Sense declaració de responsabilitatsnan
41773MONTAGUT NEBOT, María1719391940Absoltnan
1627ALMUNIA CLAVERÍA, Pabla María del Rosario1719391942Dotze anys i un dia de reclusió temporalTres anys de presó menor
1923ALTÉS GARRIGA, María del Carmen171939nanSobreseïmentnan
2012ÁLVAREZ DOLZ, Amparo1719391939Quatre mesos d'arrest majornan
3622ARNÁN PEREZ, Teresa171939nanSobreseïmentnan
3781ARRANZ RODRÍGUEZ, Carmen1719391939Absolt i dos mesos d'arrest majornan
5209BALSELLS DOLS, Josefa1719391939Llibertatnan
7404BERTOMEU ARQUES, Elvira1719391939Sobreseïmentnan
8170BOIRA ESPINET, Rosario1719391943Sobreseïmentnan
8948BORRULL MALRÀS, Francisca1719381938Llibertatnan
9850BULLICH BULLICH, María1719391941Sense declaració de responsabilitatsnan
13828CASTELLA SAGRADO, Josefa1719391940Sense declaració de responsabilitatsnan
16367COMAS SERRA, Teresa1719391939Absolt, remissió o a disposició d'altres autoritatsnan
18961DÍEZ LOZANO, Rosario1719391939Sobreseïmentnan
19800EDO CENTELLAS, Carmen1719411943Sobreseïmentnan
42007MONTESINOS PENA, Dolores1719391941Sobreseïmentnan
20370ESCUER SALOMÉ, Cecilia1719381938Absoltnan
20947ESTEVE CASTILLO, Josefina1719391939Llibertatnan
22226FERNÁNDEZ BALBOA, Conchita1719391939Llibertatnan
22578FERNÁNDEZ SANS, Ángeles1719391940Sense declaració de responsabilitatsnan
22683FERRÁNDIZ BLAS, María Isabel1719711972Pena de multa, remissió o a disposició d'altres autoritatsnan
24880FREIXEDES BERENGUER, Filomena1719391939Sis anys i un dia de presó majornan
26725GARDEÑES HUGUET, Antonia171939nanSobreseïmentnan
30452GUILLÉN ESTEBAN, Dolores1719391939Llibertatnan
31679IBÁÑEZ VIÑAS, Rosalía1719391939Sis anys i un dia de presó majornan
69272VIU ESTER, Flora1719391939Llibertatnan
33792LEMOCHE RODRÍGUEZ, Francisca1719391939Llibertatnan
34143LLASTAMOS SANROMÀ, María1719391939Absoltnan
36910MARÍN VILLARTE, María Teresa1719391939Sense declaració de responsabilitatsnan
37007MÁRQUEZ FERNÁNDEZ, Antonia1719391939Nou anys de presó majornan
39899MELICH RIVAS, Maria1719391939Sobreseïment provisional i un mes d'arrest menornan
20371ESCUER SALOMÉ, Cecilia171938nanDesglossament en un altre procedimentnan
69349VIVES FANEGA, Rosa1719401941Absoltnan
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dades.query(\"genere == 'Dona' and edat < 18\").sort_values(\"edat\").\\\n", " iloc[:,[1,5,21,22,23,24]].\\\n", " style.format({\n", " \"any_inicial\": lambda a: a.year,\n", " \"any_resol\": lambda a: a.year \n", " })\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gairebé la meitat de les encausades menors d'edat són posades en llibertat." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
numperc
pena_cat
00 mort30.7%
01 30a+51.2%
02 20-30a20.5%
03 12-20a409.5%
04 6-12a389.0%
05 6m-6a81.9%
06 <6m215.0%
07 sob7417.5%
08 lib20047.3%
09 alt327.6%
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def highlight(data) :\n", " return [\"background-color: yellow\" for a in data]\n", "\n", "\n", "menors = dades.query(\"genere == 'Dona' and edat <21\").\\\n", " groupby([\"pena_cat\"])[\"pena\"].count().\\\n", " to_frame().\\\n", " rename(columns={\"pena\" : \"num\"})\n", " \n", "menors[\"perc\"] = menors.div(menors.sum(axis=0),axis=1)\n", "\n", "menors.style.format(\"{:.1%}\",subset=[\"perc\"]).\\\n", " apply(highlight, axis=1,subset=pd.IndexSlice[\"08 lib\",:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hi ha tres condemnes a mort entre les menors, i una execució." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
cognoms_nomedatany_inicialpenaindultafusellades
10078BUSTAMANTE LÓPEZ, Concepción20.01939-12-31MortReclusió perpètua/ Trenta anys de reclusió maj...NaN
29237GONZÁLEZ RAMOS, Eugenia20.01939-12-31MortNaNexecutat/da
57250SABATÉ BOIRA, María20.01938-12-31MortReclusió perpètua/ Vint anys de reclusió menor...NaN
\n", "
" ], "text/plain": [ " cognoms_nom edat any_inicial pena \\\n", "10078 BUSTAMANTE LÓPEZ, Concepción 20.0 1939-12-31 Mort \n", "29237 GONZÁLEZ RAMOS, Eugenia 20.0 1939-12-31 Mort \n", "57250 SABATÉ BOIRA, María 20.0 1938-12-31 Mort \n", "\n", " indult afusellades \n", "10078 Reclusió perpètua/ Trenta anys de reclusió maj... NaN \n", "29237 NaN executat/da \n", "57250 Reclusió perpètua/ Vint anys de reclusió menor... NaN " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dades.query(\"genere == 'Dona' and edat <21 and pena_cat == '00 mort'\").\\\n", " iloc[:,[1,5,21,23,24,25]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Les més grans\n", "\n", "Antonia Castan Viu fou condemnada a 30 anys de presó a l’edat de 79 anys. Després li van commutar la pena a 12 anys. \n", "\n", "És un dels pocs casos que daten de l’any 1938, es a dir que va ser iniciat per les autoritats de la República.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
cognoms_nomedatany_inicialany_resolpenaindult
66666VALVERDE SAUQUILLO, Dorotea8919391939Llibertatnan
6393BAULÓ CEUMA, Josefa7919391939Absoltnan
13703CASTAN VIU, Antonia7919381938Trenta anys de reclusió majorDotze anys de presó major
13890CASTELLNOU PENA, María7919381943Sobreseïmentnan
25755GARCÍA ARASA, Teresa7819391939Sobreseïmentnan
45361ORTEGA MERCADER, Francisca7819391939Absoltnan
50102PONTE HERNÁNDEZ, Carmen7819391939Sense declaració de responsabilitatsnan
29346GONZALO ESTEBAN, Manuela7719391939Dos mesos d'arrest majornan
59032SANCHO FIBLA, Rosa7719381939Llibertatnan
62576SOLER SOLVES, María de las Nieves7719401940Sense declaració de responsabilitatsnan
34408LLOBET VALLÈS, María7619431943Sense declaració de responsabilitatsnan
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "dades.query(\"genere == 'Dona' and edat > 75 and edat<9999\").\\\n", " sort_values(\"edat\",ascending=False).\\\n", " iloc[:,[1,5,21,22,23,24]].\\\n", " style.apply(highlight, axis=1,subset=pd.IndexSlice[13703,:]).\\\n", " format({\n", " \"any_inicial\": lambda a: a.year,\n", " \"any_resol\": lambda a: a.year \n", " })" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Percentatge de dones encausades\n", "\n", "La gran majoria dels encausats en procediments judicials militars van ser homes. Només en els anys 57, 58, 60, 70 i 78 el percentatge de dones encausades supera el 10% del total. \n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "metadata": { "jupyter-vega": "#052fb4af-ba99-4898-bba4-af1c5aab18c1" }, "output_type": "display_data" }, { "data": { "application/javascript": [ "var spec = {\"config\": {\"cell\": {\"width\": 500, \"height\": 350}, \"mark\": {\"barThinSize\": 8.0, \"opacity\": 0.5}}, \"encoding\": {\"x\": {\"axis\": {\"format\": \"%Y\", \"title\": \"any\"}, \"field\": \"any_inicial\", \"scale\": {}, \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"% dones\"}, \"field\": \"perc_dones\", \"type\": \"quantitative\"}}, \"mark\": \"bar\", \"data\": {\"values\": [{\"any_inicial\": \"1936-12-31\", \"--\": null, \"Dona\": null, \"Home\": 1.0, \"perc_dones\": null}, {\"any_inicial\": \"1937-12-31\", \"--\": null, \"Dona\": 2.0, \"Home\": 28.0, \"perc_dones\": 6.666666666666667}, {\"any_inicial\": \"1938-12-31\", \"--\": null, \"Dona\": 339.0, \"Home\": 4062.0, \"perc_dones\": 7.702794819359236}, {\"any_inicial\": \"1939-12-31\", \"--\": 3.0, \"Dona\": 4109.0, \"Home\": 43192.0, \"perc_dones\": 8.68691993826769}, {\"any_inicial\": \"1940-12-31\", \"--\": 1.0, \"Dona\": 512.0, \"Home\": 8080.0, \"perc_dones\": 5.95903165735568}, {\"any_inicial\": \"1941-12-31\", \"--\": null, \"Dona\": 183.0, \"Home\": 2998.0, \"perc_dones\": 5.75290789060044}, {\"any_inicial\": \"1942-12-31\", \"--\": null, \"Dona\": 73.0, \"Home\": 1403.0, \"perc_dones\": 4.94579945799458}, {\"any_inicial\": \"1943-12-31\", \"--\": null, \"Dona\": 43.0, \"Home\": 648.0, \"perc_dones\": 6.2228654124457305}, {\"any_inicial\": \"1944-12-31\", \"--\": null, \"Dona\": 67.0, \"Home\": 816.0, \"perc_dones\": 7.587768969422424}, {\"any_inicial\": \"1945-12-31\", \"--\": null, \"Dona\": 29.0, \"Home\": 582.0, \"perc_dones\": 4.746317512274959}, {\"any_inicial\": \"1946-12-31\", \"--\": null, \"Dona\": 17.0, \"Home\": 404.0, \"perc_dones\": 4.038004750593824}, {\"any_inicial\": \"1947-12-31\", \"--\": null, \"Dona\": 19.0, \"Home\": 404.0, \"perc_dones\": 4.491725768321513}, {\"any_inicial\": \"1948-12-31\", \"--\": null, \"Dona\": 9.0, \"Home\": 208.0, \"perc_dones\": 4.147465437788019}, {\"any_inicial\": \"1949-12-31\", \"--\": null, \"Dona\": 26.0, \"Home\": 267.0, \"perc_dones\": 8.873720136518772}, {\"any_inicial\": \"1950-12-31\", \"--\": null, \"Dona\": 7.0, \"Home\": 130.0, \"perc_dones\": 5.109489051094891}, {\"any_inicial\": \"1951-12-31\", \"--\": null, \"Dona\": 6.0, \"Home\": 113.0, \"perc_dones\": 5.042016806722689}, {\"any_inicial\": \"1952-12-31\", \"--\": null, \"Dona\": 3.0, \"Home\": 53.0, \"perc_dones\": 5.357142857142857}, {\"any_inicial\": \"1953-12-31\", \"--\": null, \"Dona\": 2.0, \"Home\": 36.0, \"perc_dones\": 5.2631578947368425}, {\"any_inicial\": \"1954-12-31\", \"--\": null, \"Dona\": 2.0, \"Home\": 21.0, \"perc_dones\": 8.695652173913043}, {\"any_inicial\": \"1955-12-31\", \"--\": null, \"Dona\": null, \"Home\": 32.0, \"perc_dones\": null}, {\"any_inicial\": \"1956-12-31\", \"--\": null, \"Dona\": 12.0, \"Home\": 87.0, \"perc_dones\": 12.121212121212121}, {\"any_inicial\": \"1957-12-31\", \"--\": null, \"Dona\": 9.0, \"Home\": 62.0, \"perc_dones\": 12.67605633802817}, {\"any_inicial\": \"1958-12-31\", \"--\": null, \"Dona\": 1.0, \"Home\": 76.0, \"perc_dones\": 1.2987012987012987}, {\"any_inicial\": \"1959-12-31\", \"--\": null, \"Dona\": 1.0, \"Home\": 8.0, \"perc_dones\": 11.11111111111111}, {\"any_inicial\": \"1960-12-31\", \"--\": null, \"Dona\": null, \"Home\": 41.0, \"perc_dones\": null}, {\"any_inicial\": \"1961-12-31\", \"--\": null, \"Dona\": 1.0, \"Home\": 137.0, \"perc_dones\": 0.7246376811594203}, {\"any_inicial\": \"1962-12-31\", \"--\": null, \"Dona\": 3.0, \"Home\": 33.0, \"perc_dones\": 8.333333333333334}, {\"any_inicial\": \"1963-12-31\", \"--\": null, \"Dona\": null, \"Home\": 4.0, \"perc_dones\": null}, {\"any_inicial\": \"1964-12-31\", \"--\": null, \"Dona\": null, \"Home\": 3.0, \"perc_dones\": null}, {\"any_inicial\": \"1965-12-31\", \"--\": null, \"Dona\": null, \"Home\": 4.0, \"perc_dones\": null}, {\"any_inicial\": \"1966-12-31\", \"--\": null, \"Dona\": null, \"Home\": 1.0, \"perc_dones\": null}, {\"any_inicial\": \"1967-12-31\", \"--\": null, \"Dona\": null, \"Home\": 6.0, \"perc_dones\": null}, {\"any_inicial\": \"1968-12-31\", \"--\": 1.0, \"Dona\": null, \"Home\": 10.0, \"perc_dones\": null}, {\"any_inicial\": \"1969-12-31\", \"--\": null, \"Dona\": 10.0, \"Home\": 58.0, \"perc_dones\": 14.705882352941176}, {\"any_inicial\": \"1970-12-31\", \"--\": null, \"Dona\": null, \"Home\": 8.0, \"perc_dones\": null}, {\"any_inicial\": \"1971-12-31\", \"--\": 2.0, \"Dona\": 5.0, \"Home\": 53.0, \"perc_dones\": 8.620689655172415}, {\"any_inicial\": \"1972-12-31\", \"--\": null, \"Dona\": null, \"Home\": 10.0, \"perc_dones\": null}, {\"any_inicial\": \"1973-12-31\", \"--\": null, \"Dona\": 1.0, \"Home\": 9.0, \"perc_dones\": 10.0}, {\"any_inicial\": \"1974-12-31\", \"--\": null, \"Dona\": null, \"Home\": 4.0, \"perc_dones\": null}, {\"any_inicial\": \"1975-12-31\", \"--\": 1.0, \"Dona\": 1.0, \"Home\": 10.0, \"perc_dones\": 9.090909090909092}, {\"any_inicial\": \"1976-12-31\", \"--\": 3.0, \"Dona\": 3.0, \"Home\": 27.0, \"perc_dones\": 10.0}, {\"any_inicial\": \"1977-12-31\", \"--\": 1.0, \"Dona\": 6.0, \"Home\": 46.0, \"perc_dones\": 11.538461538461538}, {\"any_inicial\": \"1978-12-31\", \"--\": 5.0, \"Dona\": null, \"Home\": 35.0, \"perc_dones\": null}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n", "var selector = \"#052fb4af-ba99-4898-bba4-af1c5aab18c1\";\n", "var type = \"vega-lite\";\n", "\n", "var output_area = this;\n", "require(['nbextensions/jupyter-vega/index'], function(vega) {\n", " vega.render(selector, spec, type, output_area);\n", "}, function (err) {\n", " if (err.requireType !== 'scripterror') {\n", " throw(err);\n", " }\n", "});\n" ] }, "metadata": { "jupyter-vega": "#052fb4af-ba99-4898-bba4-af1c5aab18c1" }, "output_type": "display_data" }, { "data": { "image/png": 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" }, "metadata": { "jupyter-vega": "#052fb4af-ba99-4898-bba4-af1c5aab18c1" }, "output_type": "display_data" } ], "source": [ "anys = dades.groupby([\"genere\",\"any_inicial\"])[\"nom\"].count().unstack(\"genere\").reset_index()\n", "anys[\"perc_dones\"]=100*anys[\"Dona\"]/(anys[\"Dona\"]+anys[\"Home\"])\n", "\n", "\n", "alt.Chart(anys).mark_bar(barThinSize=8,opacity=0.5).encode(alt.X(\"any_inicial\",\n", " scale=alt.Scale(),\n", " axis=alt.Axis(title=\"any\",format=\"%Y\")\n", " ),\n", " alt.Y(\"perc_dones\",axis=alt.Axis(title=\"% dones\"))\n", " )\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }