Pandas

Question 1

How do you import pandas?

Answer 1

import pandas as pd

Question 2

How do you load a CSV file into a dataframe?

Answer 2

df = pd.read_csv('data.csv')

Question 3

How do you load a JSON file into a dataframe?

Answer 3

df = pd.read_json('data.json')

Question 4

How do you view the first five rows of a dataframe?

Answer 4

df.head()

Question 5

How do you view the last five rows of a dataframe?

Answer 5

df.tail()

Question 6

How do you view information about the data?

Answer 6

df.info()

Question 7

How do you return a new dataframe with no empty cells?

Answer 7

df_new = df.dropna()

Question 8

How do you return the same dataframe with no empty cells?

Answer 8

df.dropna(inplace = True)

Question 9

How do you replace empty cells with a value in the entire dataframe?

Answer 9

df.fillna("value", inplace = True)

Question 10

How do you replace empty cells in specified column(s) with a value?

Answer 10

df["column"].fillna("value", inplace = True)

Question 11

How do you calculate the MEAN of a column?

Answer 11

x = df["column"].mean()

Question 12

How do you calculate the MEDIAN of a column?

Answer 12

x = df["column"].median()

Question 13

How do you calculate the MODE of a column?

Answer 13

x = df["column"].mode()[0]

Question 14

How do you convert a column to datetime?

Answer 14

df["date"] = pd.to_datetime(df["date"])

Question 15

How do you remove NULL rows using specific column(s) as a reference?

Answer 15

df.dropna(subset=["column"...], inplace = True)

Question 16

How do you replace a specific cell value?

Answer 16

df.loc[idx, "column"] = "new_value"

Question 17

How do you loop through rows?

Answer 17

for x in df.index:
    print(x)

Question 18

How do you drop a specific row?

Answer 18

df.drop(idx, inplace = True)

Question 19

How do you view duplicate rows?

Answer 19

df.duplicated()

Question 20

How do you drop duplicated rows?

Answer 20

df.drop_duplicates(inplace = True)

Question 21

How do you drop column(s)?

Answer 21

df.drop(columns=["column", ...], inplace=True)

Question 22

How do you convert a column to a different data type?

Answer 22

df["col"] = df["col"].astype(type)

Available types: “int”, “float”, “str”, “bool”, “datetime64”

Question 23

How do you sort by column(s)?

Answer 23

df.sort_values(by=["col", ...], inplace=True)

Question 24

How do you limit a float column to x decimal places?

Answer 24

df["col"] = df["col"].round(x)

Question 25

How do you group a DataFrame by a column and calculate the mean of another column?

Answer 25

df.groupby('column_name')['another_column'].mean().reset_index()

Question 26

How do you perform multiple aggregations (mean, sum) on a column after grouping?

Answer 26

df.groupby('column_name')['another_column'].agg(['mean', 'sum']).reset_index()

Question 27

How do you specify custom names for the aggregated columns instead of using default names like ‘mean’ or ‘sum’?

Answer 27

df.groupby('column_name')['another_column'].agg(
    custom_name_1='mean', 
    custom_name_2='sum'
).reset_index()

Question 28

How do you extract the year from a datetime column?

Answer 28

df['year'] = df['datetime_column'].dt.year

Question 29

How do you extract the month from a datetime column?

Answer 29

df['month'] = df['datetime_column'].dt.month

Question 30

How do you extract the day of the week from a datetime column (where Monday is 0 and Sunday is 6)?

Answer 30

df['weekday'] = df['datetime_column'].dt.weekday

Question 31

How do you extract the day of the month from a datetime column?

Answer 31

df['day_of_month'] = df['datetime_column'].dt.day

Question 32

How do you extract the hour from a datetime column?

Answer 32

df['hour'] = df['datetime_column'].dt.hour

Question 33

How do you calculate the difference between two datetime columns (in days)?

Answer 33

df['date_diff'] = (df['datetime_column2'] - df['datetime_column1']).dt.days

Question 34

How do you filter rows where the datetime column is within a specific date range?

Answer 34

df_filtered = df[(df['datetime_column'] >= '2023-01-01') & (df['datetime_column'] <= '2023-12-31')]

Question 35

How do you get the current date and time in pandas?

Answer 35

current_datetime = pd.to_datetime('now')

Question 36

How do you add a specific number of days to a datetime column?

Answer 36

df['new_datetime'] = df['datetime_column'] + pd.Timedelta(days=7)

Question 37

How do you calculate the difference between two datetime columns in hours?

Answer 37

df['time_diff_hours'] = (df['datetime_column2'] - df['datetime_column1']).dt.total_seconds() / 3600

Question 38

How do you extract the quarter from a datetime column?

Answer 38

df['quarter'] = df['datetime_column'].dt.quarter

Question 39

How do you convert a datetime column to a string in a specific format?

Answer 39

df['datetime_str'] = df['datetime_column'].dt.strftime('%Y-%m-%d %H:%M:%S')

Question 40

How do you extract the week number from a datetime column?

Answer 40

df['week_number'] = df['datetime_column'].dt.isocalendar().week

Question 41

How do you filter rows where the datetime column is in the last 30 days?

Answer 41

df_filtered = df[df['datetime_column'] >= pd.to_datetime('today') - pd.Timedelta(days=30)]

Question 42

How do you convert a datetime column to a different timezone?

Answer 42

df['datetime_column'] = df['datetime_column'].dt.tz_localize('UTC').dt.tz_convert('America/New_York')

Question 43

How do you replace all occurrences of a specific value in a column with another value?

Answer 43

df['column_name'] = df['column_name'].replace(old_value, new_value)

Question 44

How do you filter rows based on a condition applied to a column?

Answer 44

df_filtered = df[df['column_name'] > threshold]

Question 45

How do you rename columns in a DataFrame?

Answer 45

df = df.rename(columns={'old_name': 'new_name', ...})

Question 46

How do you create a new column based on applying a function to another column?

Answer 46

df['new_column'] = df['existing_column'].apply(function)

Question 47

How do you sort a DataFrame by one or more columns?

Answer 47

df_sorted = df.sort_values(by=['column_name1', 'column_name2'], ascending=[True, False])

Question 48

How do you get the unique values from a column?

Answer 48

unique_values = df['column_name'].unique()

Question 49

How do you count the number of occurrences of each unique value in a column?

Answer 49

value_counts = df['column_name'].value_counts()

Question 50

How do you calculate the cumulative sum of a column?

Answer 50

df['cumulative_sum'] = df['column_name'].cumsum()

Question 51

How do you merge two DataFrames on multiple columns?

Answer 51

df_merged = df1.merge(df2, on=['column1', 'column2'], how='inner')

Question 52

How do you concatenate multiple DataFrames vertically (stacking them on top of each other)?

Answer 52

df_concat = pd.concat([df1, df2], ignore_index=True)

Question 53

How do you sample a random subset of rows from a DataFrame?

Answer 53

df_sample = df.sample(n=100)

Question 54

How do you create a new column with the result of the multiplication between two columns?

Answer 54

df['new_column'] = df['column1'] * df['column2']

Question 55

How do you set a specific column as the index of a DataFrame?

Answer 55

df.set_index('column_name', inplace=True)

Question 56

How do you create a new DataFrame by filtering rows based on multiple conditions?

Answer 56

df_filtered = df[(df['column1'] > threshold1) & (df['column2'] < threshold2)]