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Tableaux croisés dynamiques après les groupes
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-6
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5 files changed

+260
-6
lines changed

‎en/01-dataframe.ipynb‎

Lines changed: 46 additions & 1 deletion
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@@ -370,6 +370,49 @@
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"surveys_df.groupby(['species_id', 'sex'])###"
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]
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},
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{
374+
"cell_type": "markdown",
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"id": "7ad65751-a3da-4b2b-8f9e-7aeeb44e4f23",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"## Pivot tables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c22df2f-041f-46d0-9704-c54da1e7125e",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
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"surveys_df.groupby(\n",
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" ['species_id', 'sex']\n",
397+
")['hindfoot_length'].median()#.unstack()#.head()"
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]
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},
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{
401+
"cell_type": "code",
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"execution_count": null,
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"id": "d6ea30c5-4904-4efa-8111-611eba05f986",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
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"surveys_df.pivot_table(\n",
410+
" values='hindfoot_length',\n",
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" index='species_id',\n",
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" columns='sex',\n",
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" aggfunc='median').head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ccff77fd-99c7-428e-854e-f771da6851a1",
@@ -394,7 +437,9 @@
394437
"* **Grouping by values** of one or many columns:\n",
395438
" * `groupby(column_name)`\n",
396439
" * `groupby([column_name1, column_name2])`\n",
397-
"* **Reshaping a DataFrame** from values in the index: `unstack()`"
440+
"* **Pivot tables**\n",
441+
" * Reshaping a DataFrame from values in the index: `unstack()`\n",
442+
" * Aggregation in a pivot table: `pivot_table()`"
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]
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},
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{

‎fr/01-dataframe.ipynb‎

Lines changed: 46 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -371,6 +371,49 @@
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"surveys_df.groupby(['species_id', 'sex'])###"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9aff1b4a-4bae-4a4f-9e31-cc516daf41e5",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"## Tableaux croisés dynamiques"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c22df2f-041f-46d0-9704-c54da1e7125e",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
396+
"surveys_df.groupby(\n",
397+
" ['species_id', 'sex']\n",
398+
")['hindfoot_length'].median()#.unstack()#.head()"
399+
]
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},
401+
{
402+
"cell_type": "code",
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"execution_count": null,
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"id": "d6ea30c5-4904-4efa-8111-611eba05f986",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
410+
"surveys_df.pivot_table(\n",
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" values='hindfoot_length',\n",
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" index='species_id',\n",
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" columns='sex',\n",
414+
" aggfunc='median').head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a96f6358-ba62-4a01-b95f-df05e771fceb",
@@ -395,7 +438,9 @@
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"* **Grouper selon les valeurs** d'une ou plusieurs colonnes :\n",
396439
" * `groupby(nom_col)`\n",
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" * `groupby([nom_col1, nom_col2])`\n",
398-
"* **Transformer le DataFrame** : `unstack()`"
441+
"* **Tableaux croisés dynamiques**\n",
442+
" * Transformation selon les valeurs de l'index : `unstack()`\n",
443+
" * Aggrégation dans un tableau croisé dynamique : `pivot_table()`"
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]
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},
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{

‎solution-en/01-dataframe.ipynb‎

Lines changed: 46 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -373,6 +373,49 @@
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"surveys_df.groupby(['species_id', 'sex']).mean(numeric_only=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ad65751-a3da-4b2b-8f9e-7aeeb44e4f23",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"## Pivot tables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a4831e8b-2235-46aa-ab5e-1169edf81288",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"surveys_df.groupby(\n",
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" ['species_id', 'sex']\n",
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")['hindfoot_length'].median().unstack().head()"
401+
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d6ea30c5-4904-4efa-8111-611eba05f986",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
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"surveys_df.pivot_table(\n",
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" values='hindfoot_length',\n",
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" index='species_id',\n",
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" columns='sex',\n",
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" aggfunc='median').head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ccff77fd-99c7-428e-854e-f771da6851a1",
@@ -397,7 +440,9 @@
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"* **Grouping by values** of one or many columns:\n",
398441
" * `groupby(column_name)`\n",
399442
" * `groupby([column_name1, column_name2])`\n",
400-
"* **Reshaping a DataFrame** from values in the index: `unstack()`"
443+
"* **Pivot tables**\n",
444+
" * Reshaping a DataFrame from values in the index: `unstack()`\n",
445+
" * Aggregation in a pivot table: `pivot_table()`"
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]
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},
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{

‎solution-fr/01-dataframe.ipynb‎

Lines changed: 46 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -374,6 +374,49 @@
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"surveys_df.groupby(['species_id', 'sex']).mean(numeric_only=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9aff1b4a-4bae-4a4f-9e31-cc516daf41e5",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"## Tableaux croisés dynamiques"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a4831e8b-2235-46aa-ab5e-1169edf81288",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"surveys_df.groupby(\n",
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" ['species_id', 'sex']\n",
401+
")['hindfoot_length'].median().unstack().head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d6ea30c5-4904-4efa-8111-611eba05f986",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
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"surveys_df.pivot_table(\n",
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" values='hindfoot_length',\n",
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" index='species_id',\n",
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" columns='sex',\n",
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" aggfunc='median').head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a96f6358-ba62-4a01-b95f-df05e771fceb",
@@ -398,7 +441,9 @@
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"* **Grouper selon les valeurs** d'une ou plusieurs colonnes :\n",
399442
" * `groupby(nom_col)`\n",
400443
" * `groupby([nom_col1, nom_col2])`\n",
401-
"* **Transformer le DataFrame** : `unstack()`"
444+
"* **Tableaux croisés dynamiques**\n",
445+
" * Transformation selon les valeurs de l'index : `unstack()`\n",
446+
" * Aggrégation dans un tableau croisé dynamique : `pivot_table()`"
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]
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},
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{

‎src/01-dataframe.ipynb‎

Lines changed: 76 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -883,6 +883,76 @@
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"surveys_df.groupby(['species_id', 'sex'])###"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9aff1b4a-4bae-4a4f-9e31-cc516daf41e5",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"## Tableaux croisés dynamiques"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ad65751-a3da-4b2b-8f9e-7aeeb44e4f23",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"## Pivot tables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a4831e8b-2235-46aa-ab5e-1169edf81288",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"surveys_df.groupby(\n",
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" ['species_id', 'sex']\n",
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")['hindfoot_length'].median().unstack().head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c22df2f-041f-46d0-9704-c54da1e7125e",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
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"surveys_df.groupby(\n",
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" ['species_id', 'sex']\n",
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")['hindfoot_length'].median()#.unstack()#.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d6ea30c5-4904-4efa-8111-611eba05f986",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
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"surveys_df.pivot_table(\n",
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" values='hindfoot_length',\n",
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" index='species_id',\n",
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" columns='sex',\n",
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" aggfunc='median').head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a96f6358-ba62-4a01-b95f-df05e771fceb",
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"* **Grouper selon les valeurs** d'une ou plusieurs colonnes :\n",
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" * `groupby(nom_col)`\n",
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" * `groupby([nom_col1, nom_col2])`\n",
910-
"* **Transformer le DataFrame** : `unstack()`"
980+
"* **Tableaux croisés dynamiques**\n",
981+
" * Transformation selon les valeurs de l'index : `unstack()`\n",
982+
" * Aggrégation dans un tableau croisé dynamique : `pivot_table()`"
911983
]
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},
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{
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"* **Grouping by values** of one or many columns:\n",
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" * `groupby(column_name)`\n",
9361008
" * `groupby([column_name1, column_name2])`\n",
937-
"* **Reshaping a DataFrame** from values in the index: `unstack()`"
1009+
"* **Pivot tables**\n",
1010+
" * Reshaping a DataFrame from values in the index: `unstack()`\n",
1011+
" * Aggregation in a pivot table: `pivot_table()`"
9381012
]
9391013
},
9401014
{

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