Answer:
You can read a CSV file using the pd.read_csv function.
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())Answer:
You can handle missing values using the fillna or dropna methods.
import pandas as pd
df = pd.read_csv('data.csv')
df.fillna(0, inplace=True)
# Or to drop missing values
df.dropna(inplace=True)
print(df.head())Answer:
You can filter rows using boolean indexing.
import pandas as pd
df = pd.read_csv('data.csv')
filtered_df = df[df['column'] > 10]
print(filtered_df)Answer:
You can compute summary statistics using the describe method.
import pandas as pd
df = pd.read_csv('data.csv')
summary_stats = df.describe()
print(summary_stats)Answer:
You can group data using the groupby method and compute aggregate statistics with agg.
import pandas as pd
df = pd.read_csv('data.csv')
grouped_df = df.groupby('column').agg({'another_column': 'mean'})
print(grouped_df)Answer:
You can merge DataFrames using the merge function.
import pandas as pd
df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
merged_df = pd.merge(df1, df2, on='common_column')
print(merged_df.head())Answer:
You can concatenate DataFrames using the pd.concat function.
import pandas as pd
df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')
concatenated_df = pd.concat([df1, df2], axis=0)
print(concatenated_df.head())Answer:
You can pivot a DataFrame using the pivot_table method.
import pandas as pd
df = pd.read_csv('data.csv')
pivot_df = df.pivot_table(index='column1', columns='column2', values='value_column')
print(pivot_df)Answer:
You can melt a DataFrame using the melt method.
import pandas as pd
df = pd.read_csv('data.csv')
melted_df = df.melt(id_vars=['id'], value_vars=['column1', 'column2'])
print(melted_df)Answer:
You can create a pivot table using the pivot_table method.
import pandas as pd
df = pd.read_csv('data.csv')
pivot_table = df.pivot_table(index='column1', columns='column2', values='value_column', aggfunc='mean')
print(pivot_table)Answer:
You can apply a function using the apply method.
import pandas as pd
df = pd.read_csv('data.csv')
df['new_column'] = df['column'].apply(lambda x: x * 2)
print(df.head())Answer:
You can apply a function using the apply method with axis=1.
import pandas as pd
df = pd.read_csv('data.csv')
df['new_column'] = df.apply(lambda row: row['column1'] + row['column2'], axis=1)
print(df.head())Answer:
You can apply a function to a group using the groupby and apply methods.
import pandas as pd
df = pd.read_csv('data.csv')
grouped_df = df.groupby('column').apply(lambda x: x.mean())
print(grouped_df)Answer:
You can create a new column using np.where or pd.Series.apply.
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
df['new_column'] = np.where(df['column'] > 10, 'high', 'low')
print(df.head())Answer:
You can create a scatter plot using the plot.scatter method.
import pandas as pd
df = pd.read_csv('data.csv')
df.plot.scatter(x='column1', y='column2')Answer:
You can create a line plot using the plot.line method.
import pandas as pd
df = pd.read_csv('data.csv')
df.plot.line(x='column1', y='column2')Answer:
You can create a histogram using the plot.hist method.
import pandas as pd
df = pd.read_csv('data.csv')
df['column'].plot.hist()Answer:
You can create a box plot using the plot.box method.
import pandas as pd
df = pd.read_csv('data.csv')
df.plot.box()Answer:
You can create a bar plot using the plot.bar method.
import pandas as pd
df = pd.read_csv('data.csv')
df.plot.bar(x='column1', y='column2')Answer:
You can create a pie chart using the plot.pie method.
import pandas as pd
df = pd.read_csv('data.csv')
df['column'].value_counts().plot.pie()Answer:
You can save a DataFrame using the to_csv method.
import pandas as pd
df = pd.read_csv('data.csv')
df.to_csv('output.csv', index=False)Answer:
You can save a DataFrame using the to_excel method.
import pandas as pd
df = pd.read_csv('data.csv')
df.to_excel('output.xlsx', index=False)Answer:
You can load an Excel file using the pd.read_excel function.
import pandas as pd
df = pd.read_excel('data.xlsx')
print(df.head())Answer:
You can read specific columns using the usecols parameter in pd.read_csv.
import pandas as pd
df = pd.read_csv('data.csv', usecols=['column1', 'column2'])
print(df.head())Answer:
You can rename columns using the rename method.
import pandas as pd
df = pd.read_csv('data.csv')
df.rename(columns={'old_name': 'new_name'}, inplace=True)
print(df.head())Answer:
You can drop columns using the drop method.
import pandas as pd
df = pd.read_csv('data.csv')
df.drop(columns=['column_to_drop'], inplace=True)
print(df.head())Answer:
You can drop duplicate rows using the drop_duplicates method.
import pandas as pd
df = pd.read_csv('data.csv')
df.drop_duplicates(inplace=True)
print(df.head())Answer:
You can sort a DataFrame using the sort_values method.
import pandas as pd
df = pd.read_csv('data.csv')
df.sort_values(by='column', ascending=True, inplace=True)
print(df.head())How do you sort a DataFrame by multiple columns using pandas?
Answer:
You can sort a DataFrame by multiple columns using the sort_values method.
import pandas as pd
df = pd.read_csv('data.csv')
df.sort_values(by=['column1', 'column2'], ascending=[True, False], inplace=True)
print(df.head())Answer:
You can set a column as the index using the set_index method.
import pandas as pd
df = pd.read_csv('data.csv')
df.set_index('column', inplace=True)
print(df.head())Answer:
You can reset the index using the reset_index method.
import pandas as pd
df = pd.read_csv('data.csv')
df.reset_index(inplace=True)
print(df.head())Answer:
You can calculate the correlation matrix using the corr method.
import pandas as pd
df = pd.read_csv('data.csv')
correlation_matrix = df.corr()
print(correlation_matrix)Answer:
You can calculate the covariance matrix using the cov method.
import pandas as pd
df = pd.read_csv('data.csv')
covariance_matrix = df.cov()
print(covariance_matrix)Answer:
You can calculate the rolling mean using the rolling and mean methods.
import pandas as pd
df = pd.read_csv('data.csv')
df['rolling_mean'] = df['column'].rolling(window=3).mean()
print(df.head())Answer:
You can calculate the exponential moving average using the ewm and mean methods.
import pandas as pd
df = pd.read_csv('data.csv')
df['ema'] = df['column'].ewm(span=3, adjust=False).mean()
print(df.head())Answer:
You can calculate the cumulative sum using the cumsum method.
import pandas as pd
df = pd.read_csv('data.csv')
df['cumsum'] = df['column'].cumsum()
print(df.head())Answer:
You can calculate the cumulative product using the cumprod method.
import pandas as pd
df = pd.read_csv('data.csv')
df['cumprod'] = df['column'].cumprod()
print(df.head())Answer:
You can calculate the cumulative minimum using the cummin method.
import pandas as pd
df = pd.read_csv('data.csv')
df['cummin'] = df['column'].cummin()
print(df.head())Answer:
You can calculate the cumulative maximum using the cummax method.
import pandas as pd
df = pd.read_csv('data.csv')
df['cummax'] = df['column'].cummax()
print(df.head())Answer:
You can resample a time series using the resample method.
import pandas as pd
df = pd.read_csv('time_series_data.csv', parse_dates=['date'], index_col='date')
resampled_df = df.resample('M').mean()
print(resampled_df.head())Answer:
You can interpolate missing values using the interpolate method.
import pandas as pd
df = pd.read_csv('data.csv')
df['column'].interpolate(method='linear', inplace=True)
print(df.head())Answer:
You can calculate the rank using the rank method.
import pandas as pd
df = pd.read_csv('data.csv')
df['rank'] = df['column'].rank()
print(df.head())Answer:
You can perform a rolling window correlation using the rolling and corr methods.
import pandas as pd
df = pd.read_csv('data.csv')
rolling_corr = df['column1'].rolling(window=5).corr(df['column2'])
print(rolling_corr.head())Answer:
You can shift the values using the shift method.
import pandas as pd
df = pd.read_csv('data.csv')
df['shifted_column'] = df['column'].shift(1)
print(df.head())Answer:
You can calculate the difference using the diff method.
import pandas as pd
df = pd.read_csv('data.csv')
df['diff'] = df['column'].diff()
print(df.head())46. How do you calculate the percentage change between successive rows of a DataFrame column using pandas?
Answer:
You can calculate the percentage change using the pct_change method.
import pandas as pd
df = pd.read_csv('data.csv')
df['pct_change'] = df['column'].pct_change()
print(df.head())Answer:
You can apply a lambda function to each row using the apply method with axis=1.
import pandas as pd
df = pd.read_csv('data.csv')
df['new_column'] = df.apply(lambda row: row['column1'] + row['column2'], axis=1)
print(df.head())Answer:
You can apply a lambda function to each element using the apply method.
import pandas as pd
df = pd.read_csv('data.csv')
df['new_column'] = df['column'].apply(lambda x: x * 2)
print(df.head())Answer:
You can filter based on a string condition using boolean indexing.
import pandas as pd
df = pd.read_csv('data.csv')
filtered_df = df[df['column'].str.contains('pattern')]
print(filtered_df.head())Answer:
You can create dummy variables using the pd.get_dummies function.
import pandas as pd
df = pd.read_csv('data.csv')
dummies = pd.get_dummies(df['categorical_column'])
print(dummies.head())Answer:
You can merge DataFrames with different keys using the pd.merge function and specifying the left_on and right_on parameters.
import pandas as pd
df1 = pd.DataFrame({'key1': [1, 2, 3], 'value1': ['a', 'b', 'c']})
df2 = pd.DataFrame({'key2': [1, 2, 3], 'value2': ['x', 'y', 'z']})
merged_df = pd.merge(df1, df2, left_on='key1', right_on='key2')
print(merged_df)Answer:
You can apply a function to multiple columns using the apply method with axis=1.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df['C'] = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(df)Answer:
You can compute the moving average using the rolling and mean methods.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})
df['moving_avg'] = df['A'].rolling(window=3).mean()
print(df)Answer:
You can merge multiple
DataFrames using the pd.merge function in a loop or reduce.
import pandas as pd
from functools import reduce
dfs = [pd.DataFrame({'key': [1, 2, 3], 'value': ['a', 'b', 'c']}),
pd.DataFrame({'key': [1, 2, 3], 'value': ['x', 'y', 'z']}),
pd.DataFrame({'key': [1, 2, 3], 'value': ['u', 'v', 'w']})]
merged_df = reduce(lambda left, right: pd.merge(left, right, on='key'), dfs)
print(merged_df)Answer:
You can remove outliers by filtering based on a condition.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 100]})
q1 = df['A'].quantile(0.25)
q3 = df['A'].quantile(0.75)
iqr = q3 - q1
filtered_df = df[(df['A'] >= (q1 - 1.5 * iqr)) & (df['A'] <= (q3 + 1.5 * iqr))]
print(filtered_df)Answer:
You can reshape using the pivot method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'],
'variable': ['A', 'B', 'A', 'B'], 'value': [1, 2, 3, 4]})
wide_df = df.pivot(index='date', columns='variable', values='value')
print(wide_df)Answer:
You can stack using the melt method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-02'],
'A': [1, 3], 'B': [2, 4]})
long_df = df.melt(id_vars='date', value_vars=['A', 'B'])
print(long_df)Answer:
You can impute missing values using the fillna method.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, None, 4, 5]})
df['A'].fillna(df['A'].mean(), inplace=True)
print(df)Answer:
You can group by multiple columns using the groupby method and apply a function with apply.
import pandas as pd
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar'], 'B': ['one', 'one', 'two', 'two'], 'C': [1, 2, 3, 4]})
grouped_df = df.groupby(['A', 'B']).apply(lambda x: x.sum())
print(grouped_df)Answer:
You can concatenate DataFrames with different indices using the pd.concat function.
import pandas as pd
df1 = pd.DataFrame({'A': [1, 2, 3]}, index=[0, 1, 2])
df2 = pd.DataFrame({'B': [4, 5, 6]}, index=[2, 3, 4])
concatenated_df = pd.concat([df1, df2], axis=1)
print(concatenated_df)Answer:
You can convert a column to datetime using the pd.to_datetime function.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-02']})
df['date'] = pd.to_datetime(df['date'])
print(df)Answer:
You can extract the year using the .dt accessor.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02'])})
df['year'] = df['date'].dt.year
print(df)Answer:
You can extract the month using the .dt accessor.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-02-01'])})
df['month'] = df['date'].dt.month
print(df)Answer:
You can extract the day using the .dt accessor.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02'])})
df['day'] = df['date'].dt.day
print(df)Answer:
You can calculate the time difference using subtraction.
import pandas as pd
df = pd.DataFrame({'start': pd.to_datetime(['2021-01-01', '2021-01-02']), 'end': pd.to_datetime(['2021-01-02', '2021-01-03'])})
df['diff'] = df['end'] - df['start']
print(df)Answer:
You can round a datetime column using the round method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01 12:30:00', '2021-01-01 12:45:00'])})
df['rounded_date'] = df['date'].dt.round('H')
print(df)Answer:
You can round a datetime column using the round method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01 12:30:00', '2021-01-01 23:45:00'])})
df['rounded_date'] = df['date'].dt.round('D')
print(df)Answer:
You can round a datetime column using the round method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01 12:30:30', '2021-01-01 12:45:45'])})
df['rounded_date'] = df['date'].dt.round('min')
print(df)Answer:
You can set a datetime column as the index using the set_index method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02']), 'value': [1, 2]})
df.set_index('date', inplace=True)
print(df)Answer:
You can create a time series plot using the plot method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02']), 'value': [1, 2]})
df.set_index('date', inplace=True)
df.plot()Answer:
You can calculate the YTD sum using the cumsum method.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02', '2021-01-03']), 'value': [1, 2, 3]})
df['ytd_sum'] = df['value'].cumsum()
print(df)Answer:
You can calculate the MTD sum using the groupby and cumsum methods.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02', '202
1-02-01', '2021-02-02']), 'value': [1, 2, 3, 4]})
df['month'] = df['date'].dt.to_period('M')
df['mtd_sum'] = df.groupby('month')['value'].cumsum()
print(df)Answer:
You can calculate the WTD sum using the groupby and cumsum methods.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02', '2021-01-04', '2021-01-05']), 'value': [1, 2, 3, 4]})
df['week'] = df['date'].dt.to_period('W')
df['wtd_sum'] = df.groupby('week')['value'].cumsum()
print(df)Answer:
You can calculate the QTD sum using the groupby and cumsum methods.
import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2021-01-01', '2021-01-02', '2021-04-01', '2021-04-02']), 'value': [1, 2, 3, 4]})
df['quarter'] = df['date'].dt.to_period('Q')
df['qtd_sum'] = df.groupby('quarter')['value'].cumsum()
print(df)Answer:
You can calculate the rolling sum using the rolling and sum methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['rolling_sum'] = df['value'].rolling(window=3).sum()
print(df)Answer:
You can calculate the rolling average using the rolling and mean methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['rolling_avg'] = df['value'].rolling(window=3).mean()
print(df)Answer:
You can calculate the rolling standard deviation using the rolling and std methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['rolling_std'] = df['value'].rolling(window=3).std()
print(df)Answer:
You can calculate the exponentially weighted mean using the ewm and mean methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['ewm_mean'] = df['value'].ewm(span=3, adjust=False).mean()
print(df)79. How do you calculate the exponentially weighted standard deviation of a DataFrame column using pandas?
Answer:
You can calculate the exponentially weighted standard deviation using the ewm and std methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['ewm_std'] = df['value'].ewm(span=3, adjust=False).std()
print(df)Answer:
You can calculate the lagged difference using the shift and diff methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['lagged_diff'] = df['value'].shift(1).diff()
print(df)Answer:
You can calculate the autocorrelation using the autocorr method.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
autocorrelation = df['value'].autocorr()
print(autocorrelation)Answer:
You can create a time-lagged feature using the shift method.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['lagged_value'] = df['value'].shift(1)
print(df)Answer:
You can calculate the rank within each group using the groupby and rank methods.
import pandas as pd
df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [1, 2, 3, 4]})
df['rank_within_group'] = df.groupby('group')['value'].rank()
print(df)Answer:
You can calculate the z-score using the mean and std methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['z_score'] = (df['value'] - df['value'].mean()) / df['value'].std()
print(df)Answer:
You can calculate the rolling z-score using the rolling, mean, and std methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
rolling_mean = df['value'].rolling(window=3).mean()
rolling_std = df['value'].rolling(window=3).std()
df['rolling_z_score'] = (df['value'] - rolling_mean) / rolling_std
print(df)Answer:
You can create multiple lagged features using the shift method in a loop.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
for lag in range(1, 4):
df[f'lag_{lag}'] = df['value'].shift(lag)
print(df)87. How do you calculate the cumulative sum of values within each group of a DataFrame column using pandas?
Answer:
You can calculate the cumulative sum within each group using the groupby and cumsum methods.
import pandas as pd
df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [1, 2, 3, 4]})
df['cumsum_within_group'] = df.groupby('group')['value'].cumsum()
print(df)Answer:
You can normalize a column using the min-max normalization formula.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['normalized'] = (df['value'] - df['value'].min()) / (df['value'].max() - df['value'].min())
print(df)89. How do you standardize a DataFrame column to have a mean of 0 and a standard deviation of 1 using pandas?
Answer:
You can standardize a column using the z-score formula.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['standardized'] = (df['value'] - df['value'].mean()) / df['value'].std()
print(df)Answer:
You can apply a custom function using the rolling and apply methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['custom_rolling'] = df['value'].rolling(window=3).apply(lambda x: x.max() - x.min())
print(df)of a DataFrame column using pandas?
Answer:
You can calculate the exponentially weighted variance using the ewm and var methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['ewm_var'] = df['value'].ewm(span=3, adjust=False).var()
print(df)Answer:
You can calculate the rolling correlation using the rolling and corr methods.
import pandas as pd
df = pd.DataFrame({'value1': [1, 2, 3, 4, 5], 'value2': [5, 4, 3, 2, 1]})
df['rolling_corr'] = df['value1'].rolling(window=3).corr(df['value2'])
print(df)93. How do you calculate the cumulative product within each group of a DataFrame column using pandas?
Answer:
You can calculate the cumulative product within each group using the groupby and cumprod methods.
import pandas as pd
df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [1, 2, 3, 4]})
df['cumprod_within_group'] = df.groupby('group')['value'].cumprod()
print(df)Answer:
You can create a pivot table with multiple aggregation functions using the pivot_table method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'], 'variable': ['A', 'B', 'A', 'B'], 'value': [1, 2, 3, 4]})
pivot_table = df.pivot_table(index='date', columns='variable', values='value', aggfunc=['mean', 'sum'])
print(pivot_table)Answer:
You can calculate the expanding mean using the expanding and mean methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['expanding_mean'] = df['value'].expanding().mean()
print(df)Answer:
You can calculate the expanding sum using the expanding and sum methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['expanding_sum'] = df['value'].expanding().sum()
print(df)Answer:
You can calculate the expanding standard deviation using the expanding and std methods.
import pandas as pd
df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
df['expanding_std'] = df['value'].expanding().std()
print(df)Answer:
You can create a pivot table with multiple index levels using the pivot_table method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'], 'variable': ['A', 'B', 'A', 'B'], 'value': [1, 2, 3, 4]})
pivot_table = df.pivot_table(index=['date', 'variable'], values='value', aggfunc='mean')
print(pivot_table)Answer:
You can create a pivot table with multiple value columns using the pivot_table method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'], 'variable': ['A', 'B', 'A', 'B'], 'value1': [1, 2, 3, 4], 'value2': [5, 6, 7, 8]})
pivot_table = df.pivot_table(index='date', columns='variable', values=['value1', 'value2'], aggfunc='mean')
print(pivot_table)100. How do you create a pivot table with multiple aggregation functions for multiple value columns using pandas?
Answer:
You can create a pivot table with multiple aggregation functions for multiple value columns using the pivot_table method.
import pandas as pd
df = pd.DataFrame({'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02'], 'variable': ['A', 'B', 'A', 'B'], 'value1': [1, 2, 3, 4], 'value2': [5, 6, 7, 8]})
pivot_table = df.pivot_table(index='date', columns='variable', values=['value1', 'value2'], aggfunc={'value1': 'mean', 'value2': 'sum'})
print(pivot_table)If you found this repository helpful, please give it a star!
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