252 lines
67 KiB
Plaintext
252 lines
67 KiB
Plaintext
{
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"cells": [
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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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"source": [
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"# Learning Practice 2 for the University of Tulsa's QM-7063 Data Mining Course\n",
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"# Dimension Reduction\n",
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"# Professor: Dr. Abdulrashid, Spring 2023\n",
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"# Noah L. Schrick - 1492657\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.decomposition import PCA\n",
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"import matplotlib.pyplot as plt"
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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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"## Read in Breakfast Cereal data\n",
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"cereals_df = pd.read_csv('Cereals.csv')\n"
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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/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>mean</th>\n",
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" <th>median</th>\n",
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" <th>min</th>\n",
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" <th>max</th>\n",
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" <th>sd</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>calories</th>\n",
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" <td>106.883117</td>\n",
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" <td>110.00</td>\n",
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" <td>50.00</td>\n",
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" <td>160.0</td>\n",
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" <td>19.484119</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>sodium</th>\n",
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" <td>159.675325</td>\n",
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" <td>180.00</td>\n",
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" <td>0.00</td>\n",
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" <td>320.0</td>\n",
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" <td>83.832295</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>protein</th>\n",
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" <td>2.545455</td>\n",
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" <td>3.00</td>\n",
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" <td>1.00</td>\n",
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" <td>6.0</td>\n",
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" <td>1.094790</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>fat</th>\n",
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" <td>1.012987</td>\n",
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" <td>1.00</td>\n",
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" <td>0.00</td>\n",
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" <td>5.0</td>\n",
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" <td>1.006473</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>fiber</th>\n",
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" <td>2.151948</td>\n",
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" <td>2.00</td>\n",
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" <td>0.00</td>\n",
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" <td>14.0</td>\n",
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" <td>2.383364</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>carbo</th>\n",
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" <td>14.802632</td>\n",
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" <td>14.50</td>\n",
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" <td>5.00</td>\n",
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" <td>23.0</td>\n",
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" <td>3.907326</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>sugars</th>\n",
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" <td>7.026316</td>\n",
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" <td>7.00</td>\n",
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" <td>0.00</td>\n",
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" <td>15.0</td>\n",
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" <td>4.378656</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>potass</th>\n",
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" <td>98.666667</td>\n",
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" <td>90.00</td>\n",
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" <td>15.00</td>\n",
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" <td>330.0</td>\n",
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" <td>70.410636</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>weight</th>\n",
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" <td>1.029610</td>\n",
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" <td>1.00</td>\n",
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" <td>0.50</td>\n",
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" <td>1.5</td>\n",
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" <td>0.150477</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>cups</th>\n",
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" <td>0.821039</td>\n",
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" <td>0.75</td>\n",
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" <td>0.25</td>\n",
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" <td>1.5</td>\n",
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" <td>0.232716</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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" mean median min max sd\n",
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"calories 106.883117 110.00 50.00 160.0 19.484119\n",
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"sodium 159.675325 180.00 0.00 320.0 83.832295\n",
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"protein 2.545455 3.00 1.00 6.0 1.094790\n",
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"fat 1.012987 1.00 0.00 5.0 1.006473\n",
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"fiber 2.151948 2.00 0.00 14.0 2.383364\n",
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"carbo 14.802632 14.50 5.00 23.0 3.907326\n",
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"sugars 7.026316 7.00 0.00 15.0 4.378656\n",
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"potass 98.666667 90.00 15.00 330.0 70.410636\n",
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"weight 1.029610 1.00 0.50 1.5 0.150477\n",
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"cups 0.821039 0.75 0.25 1.5 0.232716"
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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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"## Compute mean, median, min, max, and standard deviation for quantitative variables\n",
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"quan_df = cereals_df[[\"calories\", \"sodium\", \"protein\", \"fat\", \"fiber\", \"carbo\", \"sugars\", \"potass\", \"weight\", \"cups\"]]\n",
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"pd.DataFrame({'mean': quan_df.mean(),\n",
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"'median': quan_df.median(),\n",
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"'min': quan_df.min(),\n",
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"'max': quan_df.max(),\n",
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"'sd': quan_df.std()\n",
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"})"
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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": 22,
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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": [
|
|
"<Figure size 1000x700 with 10 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"## Histogram for each of the quant vars\n",
|
|
"quant_vars = [\"calories\", \"sodium\", \"protein\", \"fat\", \"fiber\", \"carbo\", \"sugars\", \"potass\", \"weight\", \"cups\"]\n",
|
|
"rowcounter = 0\n",
|
|
"colcounter = 0\n",
|
|
"\n",
|
|
"# 2 columns\n",
|
|
"cols = 2\n",
|
|
"rows = len(quant_vars) // cols\n",
|
|
"if (len(quant_vars) % cols != 0):\n",
|
|
" rows += 1\n",
|
|
"\n",
|
|
"fig, axes = plt.subplots(nrows=int(rows), ncols=int(cols))\n",
|
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"\n",
|
|
"for var in quant_vars:\n",
|
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" ax = cereals_df[var].hist(ax=axes[int(rowcounter)][int(colcounter)], figsize=(10,7))\n",
|
|
" ax.set_xlabel(var); ax.set_ylabel('Count') # set x and y-axis label\n",
|
|
"\n",
|
|
" if (rowcounter == (rows-1)):\n",
|
|
" rowcounter = 0\n",
|
|
" colcounter += 1\n",
|
|
" else:\n",
|
|
" rowcounter += 1\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n",
|
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" \n"
|
|
]
|
|
}
|
|
],
|
|
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