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Group by and pivot for bins of data

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Noah L. Schrick 2023-01-24 18:15:12 -06:00
parent b7b8769e63
commit d612119625
1 changed files with 165 additions and 5 deletions
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Schrick-Noah_Lecture-Work.ipynb
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@ -2,7 +2,7 @@
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@ -12,12 +12,13 @@
"# Noah L. Schrick - 1492657\n", "# Noah L. Schrick - 1492657\n",
"\n", "\n",
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt" "import matplotlib.pyplot as plt"
] ]
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@ -229,7 +230,7 @@
"CAT. MEDV 0 " "CAT. MEDV 0 "
] ]
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@ -570,7 +571,7 @@
"CAT. MEDV -0.44 -0.47 0.79 1.00 " "CAT. MEDV -0.44 -0.47 0.79 1.00 "
] ]
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@ -579,6 +580,153 @@
"# Correlation Matrix\n", "# Correlation Matrix\n",
"bostonHousing_df.corr().round(2)" "bostonHousing_df.corr().round(2)"
] ]
},
{
"cell_type": "code",
"execution_count": 7,
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{
"data": {
"text/plain": [
"RM_bin CHAS\n",
"3 0 25.300000\n",
"4 0 15.407143\n",
"5 0 17.200000\n",
" 1 22.218182\n",
"6 0 21.769170\n",
" 1 25.918750\n",
"7 0 35.964444\n",
" 1 44.066667\n",
"8 0 45.700000\n",
" 1 35.950000\n",
"Name: MEDV, dtype: float64"
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"source": [
"# Create bins of size 1 for variables\n",
"\n",
"bostonHousing_df['RM_bin'] = pd.cut(bostonHousing_df.RM, range(0, 10), labels=False)\n",
"\n",
"# Compute average of MEDV by (binned) RM and CHAS. \n",
"# Group the data using groupby, then restrict the analysis to MEDV and determine the mean for each group.\n",
"\n",
"bostonHousing_df.groupby(['RM_bin', 'CHAS'])['MEDV'].mean()\n"
]
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>CHAS</th>\n",
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"text/plain": [
"CHAS 0 1 All\n",
"RM_bin \n",
"3 25.300000 NaN 25.300000\n",
"4 15.407143 NaN 15.407143\n",
"5 17.200000 22.218182 17.551592\n",
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"# create bins of size 1 for RM\n",
"bostonHousing_df['RM_bin'] = pd.cut(bostonHousing_df.RM, range(0, 10), labels=False)\n",
"\n",
"# use pivot_table() to reshape data and generate pivot table\n",
"pd.pivot_table(bostonHousing_df, values='MEDV', index=['RM_bin'], columns=['CHAS'], aggfunc=np.mean, margins=True)"
]
} }
], ],
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