Dataframe in eine pickle datei gespeichert
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# Erlaube .ipynb-Dateien
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# Erlaube .ipynb-Dateien
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!*.ipynb
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!*.ipynb
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!*.pickle
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@ -124,20 +124,12 @@
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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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"cell_type": "code",
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"execution_count": 41,
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"execution_count": null,
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"id": "2adae4ff",
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"id": "2adae4ff",
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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},
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"outputs": [
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"outputs": [],
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Es wurden 1034 Dateien eingelesen.\n"
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]
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}
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],
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"source": [
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"source": [
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"import numpy as np\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pandas as pd\n",
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@ -181,95 +173,37 @@
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"\n",
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"\n",
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"# Display the first few rows of the DataFrame\n",
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"# Display the first few rows of the DataFrame\n",
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"#print(df.head(n=20))\n",
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"#print(df.head(n=20))\n",
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"rick = np.array(rick)\n",
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"#rick = np.array(rick)\n",
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"\n",
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"\n",
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"# Speichern der 'kirp' Liste in einer Pickle-Datei\n",
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"# Speichern der 'kirp' Liste in einer Pickle-Datei\n",
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"with open('rick.pickle', 'wb') as f:\n",
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"#with open('rick.pickle', 'wb') as f:\n",
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" pickle.dump(rick, f)\n"
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"# pickle.dump(rick, f)\n"
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]
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]
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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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"cell_type": "code",
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"execution_count": 42,
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"execution_count": null,
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"id": "dfe4f964-6068-46da-8103-194525086f01",
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"id": "dfe4f964-6068-46da-8103-194525086f01",
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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},
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"outputs": [
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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>genome_frequencies</th>\n",
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" <th>cancer_type</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>[20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,...</td>\n",
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" <td>kirp</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>[37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29....</td>\n",
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" <td>kirp</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>[45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8...</td>\n",
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" <td>kirp</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>[15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66...</td>\n",
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" <td>kirp</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>[35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18....</td>\n",
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" <td>kirp</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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" genome_frequencies cancer_type\n",
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"0 [20.331, 0.0, 25.1806, 1.1301, 0.4836, 7.3269,... kirp\n",
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"1 [37.0405, 0.5002, 77.4246, 4.2188, 1.0408, 29.... kirp\n",
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"2 [45.4456, 0.0903, 74.9545, 4.843, 1.5188, 11.8... kirp\n",
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"3 [15.2345, 0.3393, 62.0003, 2.4412, 0.932, 2.66... kirp\n",
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"4 [35.0709, 0.2333, 62.8022, 2.8872, 1.0547, 18.... kirp"
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]
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},
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"execution_count": 42,
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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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"source": [
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"data_Frame = pd.DataFrame(data, columns=[\"genome_frequencies\", \"cancer_type\"])\n",
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"data_Frame = pd.DataFrame(data, columns=[\"genome_frequencies\", \"cancer_type\"])\n",
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"data_Frame.head()"
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"data_Frame.head()"
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]
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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": null,
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"id": "0f5cc92a-4485-4184-845e-116ea9a9776d",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('rick.pickle', 'wb') as f:\n",
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" pickle.dump(rick, f)"
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "c60cbf60-d904-4ee0-8f70-588bb109368b",
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"id": "c60cbf60-d904-4ee0-8f70-588bb109368b",
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