Pandas

Question 1

Filter df for when Column is null

Answer 1

df[ df.Column.isnull() ]

Question 2

Filter df for when Column is not null

Answer 2

df[ df.Column.notna() ]

Question 3

Create a boolean series for when colA >100 AND colB <0

Answer 3

(df.colA >100 & df.colB <0)

Question 4

Create a boolean series for when colA >100 OR colB <0

Answer 4

(df.colA >100 | df.colB <0)

Question 5

Return a dataframe’s data types

Answer 5

df.dtypes

Question 6

Return the dimensions of a dataframe

Answer 6

df.shape

Question 7

Rename column MANUfacturer as ‘manufacturer’

Answer 7

df.rename( columns= {‘MANUfacturer’ : ‘manufacturer’}, inplace=True)

Question 8

Convert a string column to a float

Answer 8

df[‘column’] = df[‘column’].astype(float)

Question 9

Extract first prefix when string column is split by a dash

Answer 9

df.column.str.split(‘-‘).str[0]

Question 10

Replace values in Column using a mapping dictionary

Answer 10

df.column = df.column.map( {‘Key’ : ‘newkey’, ‘Key1’ : ‘newkey1’ } )

Question 11

Export dataframe to csv file without index values

Answer 11

df.to_csv(‘filename.csv’, index=False)

Question 12

Get meta-data information for the columns of a dataframe

Answer 12

df.info()

Question 13

Get the name of the columns in a dataframe

Answer 13

df.columns

Question 14

Get descriptive statistics for a column

Answer 14

df.column.describe()

Question 15

Get frequencies for each unique value in a column

Answer 15

df.column.value_counts()

Question 16

Get the averages of col_B grouped by col_A

Answer 16

df.groupby(df.col_A).col_B.mean()

Question 17

Apply the size, min, and max functions to the dataframe grouped by col_A

Answer 17

df.groupby(df.col_A).agg( [‘size’, min, max] )

Question 18

Create a pivot table where col_V1 is sumed and col_V2 shows the min and max. Have col_1 and col_2 as rows and col_A and col_B as columns. Include grand totals.

Answer 18

df.pivot_table(
     values=['col_V1', col_V2'],
     index=['col_1', 'col_2'],
     columns=['col_A', 'col_B'],
    aggfunc={
          'col_v1': sum, 
          'col_V2': [min, max] },
     margins=True)

Question 19

List the index (aka row labels) of a dataframe

Answer 19

df.index

Question 20

Convert a dataframe (or series) into a numpy array

Answer 20

df.to_numpy()

Question 21

Assign colA and colB as the index (multiIndex) of the dataframe

Answer 21

df.set_index( [‘colA’, ‘colB’], inplace=True)

Question 22

Vertically append two dataframes and assign an additional index indicating which df the row came from

Answer 22

pd.concat( [df1,df2], keys=[1, 2])

Question 23

Do an inner join on the indexes two dataframes and add a suffix to duplicated column names

Answer 23

df1.merge(df2,
left_index=True,
right_index=True,
suffixes= (‘_df1’,’_df2’))

Question 24

Apply a function element-wise to a series

Answer 24

df. col_name.apply(function_name)
- - OR –
df. col_name.map(function_name)

Question 25

Apply a function element-wise to a dataframe

Answer 25

df.applymap(function_name)

Question 26

Apply a function along the columns of a dataframe

Answer 26

df.apply(function_name)

Question 27

Unpivot a dataframe and rename the variables as ID and the values as ‘fact’

Answer 27

df.melt( id_vars=[col1, col2],
value_vars=[col3, col4], #defaults to all non-id_vars
var_name= ‘ID’,
value_name = ‘fact’)

Question 28

Return a boolean mask if a regex pattern is found in a certain column

Answer 28

df[col_name].str.contains(pattern)

Question 29

Extract a regex capture group from a column

Answer 29

df[col_name].str.extract(pattern)

Question 30

Extract more than one group of patterns from a column

Answer 30

df[col_name].str.extract(pattern_with_multiple_capture_groups)

Question 31

Replace a regex or string in a column with another string

Answer 31

df[col_name].str.replace(pattern, replacement_string)

Question 32

Calculate the number of missing values in each column

Answer 32

df.isnull().sum()

Question 33

Drop rows with any missing values

Answer 33

df.dropna()

Question 34

Drop specific columns

Answer 34

df.drop(columns_to_drop, axis=1)

Question 35

Drop columns with less than a certain number of non-null value

Answer 35

df.dropna(thresh = min_nonnull, axis=1)

Question 36

Replace missing values in a column with another value

Answer 36

df[col_name].fillna(replacement_value)

Question 37

Show all duplicate rows in a dataframe

Answer 37

df[ df.duplicated( keep=False ) ]

Question 38

Drop rows with duplicate values in only certain columns. Keep the last duplicate row

Answer 38

df.drop_duplicates( [col_1, col_2], keep=’last’)

Question 39

Replace values of column_A with values of column B when column_A is less than zero

Answer 39

df. column_A = df.column_A.mask(
df. column_A < 0,
df. column_b)

Question 40

Resetting the index

Answer 40

df.reset_index(inplace=True)

Question 41

Do a left join on a shared column named ‘ID;

Answer 41

df1.merge( df2, on=’ID’, how-‘left’)

Question 42

Fill in missing values of a datafram with zeros

Answer 42

df.fillna(0, inplace=True)

Question 43

Find the correlations between columns in a dataset

Answer 43

df.corr()

Question 44

Convert a column if a dataframe into a list

Answer 44

new_list = df.column.tolist()

Question 45

Get all rows of a dataframe where the value of a column is not in the elements of a list

Answer 45

df[ ~df.column_name.isna( [list_values] ) ]

Question 46

Sort a dataframe by a col_A descinding and col_B ascending and reset the index

Answer 46

df.sort_values( [‘col_A’, ‘col_B’],
ascending=[False, True],
ignore_index=True,
inplace=True)