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‎Pandas_Tutorial.ipynb

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@@ -6,7 +6,7 @@
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"name": "Pandas Tutorial.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"authorship_tag": "ABX9TyO8dbP1sfunTXTjSivB0W/Q",
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"authorship_tag": "ABX9TyMZ1qeD4j0AhLXdG2qW342F",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -68,7 +68,7 @@
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"```\n",
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"!pip install pandas\n",
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"```\n",
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"and press enter to install library if not available in your workspace."
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"and press shift + enter to install library if not available in your workspace."
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]
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},
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{
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"source": [
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"import pandas as pd"
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],
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"execution_count": 4,
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"execution_count": null,
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"outputs": []
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},
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{
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"# df = pd.read_csv('Advertising.csv', index_col =\"TV\" ) # For custom index column\n",
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"#print(df)\n"
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],
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"execution_count": 5,
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"execution_count": null,
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"outputs": []
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},
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{
@@ -147,7 +147,7 @@
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"df.head()# By default, shows first 5 rows of data\n",
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"#df.tail()# By default, shows last 5 rows of data "
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],
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"execution_count": 6,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.columns"
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],
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"execution_count": 7,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.shape"
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],
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"execution_count": 8,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.info()"
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],
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"execution_count": 9,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"df.describe()\n",
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"# df.describe().T # For transpose view of the same"
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],
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"execution_count": 10,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df.isnull().values.any() # If any value is missing\n",
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"df.isnull().sum() # Total missing values"
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],
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"execution_count": 11,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df.drop([198,199], inplace =True) # rows with index labeled as 198 and 199 will be dropped\n",
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"df.shape"
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],
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"execution_count": 12,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df = df.drop(drop_list, axis=1)\n",
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"df.columns # Unnamed column will be dropped"
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],
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"execution_count": 13,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df.drop(df.columns[[0, 2]], axis = 1, inplace = True)\n",
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"df.columns "
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],
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"execution_count": 14,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df = pd.read_csv('Advertising.csv')\n",
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"print(df.head())"
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],
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"execution_count": 15,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"data = df[['TV', 'radio']]\n",
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"data"
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],
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"execution_count": 16,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.sort_values(['radio', 'TV'], ascending = False)# Set ascending true or false to get different order"
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],
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"execution_count": 17,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df[df.radio >= 49]# Can also use >,<, <=,=="
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],
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"execution_count": 18,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df[df['TV'].between(239,248)]"
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],
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"execution_count": 19,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.query('239 <= TV <= 248')"
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],
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"execution_count": 20,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df[(df['TV'] >= 239) & (df['TV'] <= 248)]"
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],
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"execution_count": 21,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.iloc[: , 0:3]"
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],
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"execution_count": 22,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"# use negative to exclude the last 3 columns\n",
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"df.iloc[: , :-3]"
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],
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"execution_count": 23,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.iloc[0:3 , :]"
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],
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"execution_count": 24,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.iloc[:-3 , :]"
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],
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"execution_count": 25,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.sum(axis=0)#axis 1 for rows"
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],
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"execution_count": 27,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df['TV'].sum()"
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],
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"execution_count": 28,
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"execution_count": null,
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"outputs": [
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"output_type": "execute_result",
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"source": [
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"df.TV.mean()"
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],
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"execution_count": 29,
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"execution_count": null,
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"outputs": [
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"output_type": "execute_result",
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"source": [
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"df.TV.median()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df.TV.mode()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"source": [
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"df['TV'].corr(df['radio'], method='kendall')# method pearson, spearman"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",

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