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count and percentage
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‎notebooks/Pandas count and percentage by value for a column.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Pandas count and percentage by value for a column\n",
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"\n",
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"* read remote data from pdf\n",
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"* calculate count and percent\n",
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"* format percent in better output\n",
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"\n",
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"Bonus\n",
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"\n",
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"* pandas column renaming"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>food</th>\n",
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" <th>Portion size</th>\n",
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" <th>per 100 grams</th>\n",
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" <th>energy</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Fish cake</td>\n",
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" <td>90 cals per cake</td>\n",
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" <td>200 cals</td>\n",
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" <td>Medium</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Fish fingers</td>\n",
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" <td>50 cals per piece</td>\n",
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" <td>220 cals</td>\n",
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" <td>Medium</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Gammon</td>\n",
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" <td>320 cals</td>\n",
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" <td>280 cals</td>\n",
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" <td>Med-High</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Haddock fresh</td>\n",
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" <td>200 cals</td>\n",
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" <td>110 cals</td>\n",
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" <td>Low calorie</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Halibut fresh</td>\n",
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" <td>220 cals</td>\n",
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" <td>125 cals</td>\n",
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" <td>Low calorie</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" food Portion size per 100 grams energy\n",
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"0 Fish cake 90 cals per cake 200 cals Medium\n",
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"1 Fish fingers 50 cals per piece 220 cals Medium\n",
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"2 Gammon 320 cals 280 cals Med-High\n",
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"3 Haddock fresh 200 cals 110 cals Low calorie\n",
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"4 Halibut fresh 220 cals 125 cals Low calorie"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from tabula import read_pdf\n",
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"import pandas as pd\n",
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"df = read_pdf(\"http://www.uncledavesenterprise.com/file/health/Food%20Calories%20List.pdf\", pages=3, pandas_options={'header': None})\n",
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"df.columns = ['food', 'Portion size ', 'per 100 grams', 'energy']\n",
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"df.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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"s = df.energy"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Medium 14\n",
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"High 6\n",
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"Low calorie 4\n",
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"Med-High 4\n",
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"Low-Med 1\n",
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"Low- Med 1\n",
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"Name: energy, dtype: int64"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"counts = s.value_counts()\n",
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"counts"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Medium 0.466667\n",
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"High 0.200000\n",
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"Low calorie 0.133333\n",
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"Med-High 0.133333\n",
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"Low-Med 0.033333\n",
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"Low- Med 0.033333\n",
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"Name: energy, dtype: float64"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"percent = s.value_counts(normalize=True)\n",
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"percent"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Medium 46.7%\n",
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"High 20.0%\n",
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"Low calorie 13.3%\n",
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"Med-High 13.3%\n",
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"Low-Med 3.3%\n",
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"Low- Med 3.3%\n",
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"Name: energy, dtype: object"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"percent100 = s.value_counts(normalize=True).mul(100).round(1).astype(str) + '%'\n",
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"percent100"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>counts</th>\n",
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" <th>per</th>\n",
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" <th>per100</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Medium</th>\n",
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" <td>14</td>\n",
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" <td>0.466667</td>\n",
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" <td>46.7%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>High</th>\n",
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" <td>6</td>\n",
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" <td>0.200000</td>\n",
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" <td>20.0%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Low calorie</th>\n",
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" <td>4</td>\n",
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" <td>0.133333</td>\n",
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" <td>13.3%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Med-High</th>\n",
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" <td>4</td>\n",
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" <td>0.133333</td>\n",
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" <td>13.3%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Low-Med</th>\n",
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" <td>1</td>\n",
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" <td>0.033333</td>\n",
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" <td>3.3%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Low- Med</th>\n",
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" <td>1</td>\n",
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" <td>0.033333</td>\n",
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" <td>3.3%</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" counts per per100\n",
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"Medium 14 0.466667 46.7%\n",
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"High 6 0.200000 20.0%\n",
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"Low calorie 4 0.133333 13.3%\n",
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"Med-High 4 0.133333 13.3%\n",
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"Low-Med 1 0.033333 3.3%\n",
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"Low- Med 1 0.033333 3.3%"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pd.DataFrame({'counts': counts, 'per': percent, 'per100': percent100})"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"s = df.energy\n",
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"counts = s.value_counts()\n",
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"percent = s.value_counts(normalize=True)\n",
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"percent100 = s.value_counts(normalize=True).mul(100).round(1).astype(str) + '%'\n",
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"pd.DataFrame({'counts': counts, 'per': percent, 'per100': percent100})"
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]
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}
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],
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"metadata": {
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"display_name": "Python 3",
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"language": "python",
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"nbformat": 4,
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