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simecek/iteration_on_combinations.ipynb

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iteration_on_combinations.ipynb
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"colab": {
"name": "Iteration_on_combinations.ipynb",
"provenance": [],
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"authorship_tag": "ABX9TyOVllpsnDPlkrxKNRk8GwRq",
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"cell_type": "markdown",
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"<a href=\"https://colab.research.google.com/gist/simecek/65e80cf9693099c7ec062bd7e52c9d0d/iteration_on_combinations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
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{
"cell_type": "markdown",
"metadata": {
"id": "IXRY-TIA5M5x",
"colab_type": "text"
},
"source": [
"Just a small example how to iterate through pairs/combinations of DataFrame columns instead of having multiple for-loops.\n",
"\n",
"The original source is https://stackoverflow.com/a/45350450."
]
},
{
"cell_type": "code",
"metadata": {
"id": "jPyQ57qdsSVq",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.stats import ttest_ind\n",
"from itertools import combinations"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5g8h6Yy-shPT",
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},
"source": [
"# generate a random array, add a small constant to each column\n",
"N, M = 20, 4\n",
"A = np.random.randn(N, M) + np.arange(M)/4\n",
"\n",
"# converts numpy array to pandas df\n",
"df = pd.DataFrame(A)\n",
"df"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
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"12 0.054816 -1.029457 0.057920 2.209974\n",
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"19 1.178102 -0.024724 -0.977254 0.538408"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AKPbk8ei1QHI",
"colab_type": "code",
"outputId": "93969cc8-1c9e-4154-b271-707e7b18776a",
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"height": 166
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},
"source": [
"pairwise_pvalues = pd.DataFrame(columns=df.columns, index=df.columns, dtype=float)\n",
"\n",
"for (label1, column1), (label2, column2) in combinations(df.items(), 2):\n",
" pairwise_pvalues.loc[label1, label2] = ttest_ind(column1, column2)[1]\n",
" pairwise_pvalues.loc[label2, label1] = pairwise_pvalues.loc[label1, label2]\n",
"\n",
"pairwise_pvalues.round(3)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"3 0.039 0.111 0.730 NaN"
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},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
}
]
}
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