pandas

Question 1

Importing Data

From a CSV file

Answer 1

pd.read_csv(filename)

Question 2

Importing Data

From a delimited text file (like TSV)

Answer 2

pd.read_table(filename)

Question 3

Importing Data

From an Excel file

Answer 3

pd.read_excel(filename)

Question 4

Importing Data

Read from a SQL table/database

Answer 4

pd.read_sql(query, connection_object)

Question 5

Importing Data

Read from a JSON formatted string, URL or file.

Answer 5

pd.read_json(json_string)

Question 6

Importing Data

Parses an html URL, string or file and extracts tables to a list of dataframes

Answer 6

pd.read_html(url)

Question 7

Importing Data

Takes the contents of your clipboard and passes it to read_table()

Answer 7

pd.read_clipboard()

Question 8

Importing Data

From a dict, keys for columns names, values for data as lists

Answer 8

pd.DataFrame(dict)

Question 9

Exporting Data

Write to a CSV file

Answer 9

df.to_csv(filename)

Question 10

Exporting Data

Write to an Excel file

Answer 10

df.to_excel(filename)

Question 11

Exporting Data

Write to a SQL table

Answer 11

df.to_sql(table_name, connection_object)

Question 12

Exporting Data

Write to a file in JSON format

Answer 12

df.to_json(filename)

Question 13

Create Test Objects

Create a series from an iterable my_list

Answer 13

pd.Series(my_list)

Question 14

Create Test Objects

5 columns and 20 rows of random floats

Answer 14

pd.DataFrame(np.random.rand(20,5))

Question 15

Create Test Objects

Add a date index

Answer 15

df.index = pd.date_range(‘1900/1/30’, periods=df.shape[0])

Question 16

Viewing/Inspecting Data

First n rows of the DataFrame

Answer 16

df.head(n)

Question 17

Last n rows of the DataFrame

Answer 17

df.tail(n)

Question 18

Number of rows and columns

Answer 18

df.shape

Question 19

Index, Datatype and Memory information

Answer 19

df.info()

Question 20

Summary statistics for numerical columns

Answer 20

df.describe()

Question 21

View unique values and counts

Answer 21

s.value_counts(dropna=False)

Question 22

Unique values and counts for all columns

Answer 22

df.apply(pd.Series.value_counts)

Question 23

Returns column with label col as Series

Answer 23

df[col]

Question 24

Returns columns as a new DataFrame

Answer 24

df[[col1, col2]]

Question 25

Selection by position

Answer 25

s.iloc[0]

Question 26

Selection by index

Answer 26

s.loc[‘index_one’]

Question 27

First row

Answer 27

df.iloc[0,:]

Question 28

First element of first column

Answer 28

df.iloc[0,0]

Question 29

Rename columns

Answer 29

df.columns = [‘a’,’b’,’c’]

Question 30

Checks for null Values, Returns Boolean Arrray

Answer 30

pd.isnull()

Question 31

Opposite of pd.isnull()

Answer 31

pd.notnull()

Question 32

Drop all rows that contain null values

Answer 32

df.dropna()

Question 33

Drop all columns that contain null values

Answer 33

df.dropna(axis=1)

Question 34

Drop all rows have have less than n non null values

Answer 34

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

Question 35

Replace all null values with x

Answer 35

df.fillna(x)

Question 36

Replace all null values with the mean (mean can be replaced with almost any function from the statistics section)

Answer 36

s.fillna(s.mean())

Question 37

Convert the datatype of the series to float

Answer 37

s.astype(float)

Question 38

Replace all values equal to 1 with ‘one’

Answer 38

s.replace(1,’one’)

Question 39

Replace all 1 with ‘one’ and 3 with ‘three’

Answer 39

s.replace([1,3],[‘one’,’three’])

Question 40

Mass renaming of columns

Answer 40

df.rename(columns=lambda x: x + 1)

Question 41

Selective renaming

Answer 41

df.rename(columns={‘old_name’: ‘new_ name’})

Question 42

Change the index

Answer 42

df.set_index(‘column_one’)

Question 43

Mass renaming of index

Answer 43

df.rename(index=lambda x: x + 1)

Question 44

Rows where the column col is greater than 0.5

Answer 44

df[df[col] > 0.5]

Question 45

Rows where 0.7 > col > 0.5

Answer 45

df[(df[col] > 0.5) & (df[col] < 0.7)]

Question 46

Sort values by col1 in ascending order

Answer 46

df.sort_values(col1)

Question 47

Sort values by col2 in descending order

Answer 47

df.sort_values(col2,ascending=False)

Question 48

Sort values by col1 in ascending order then col2 in descending order

Answer 48

df.sort_values([col1,col2],ascending=[True,False])

Question 49

Returns a groupby object for values from one column

Answer 49

df.groupby(col)

Question 50

Returns groupby object for values from multiple columns

Answer 50

df.groupby([col1,col2])

Question 51

Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics section)

Answer 51

df.groupby(col1)[col2]

Question 52

Create a pivot table that groups by col1 and calculates the mean of col2 and col3
df.groupby(col1).agg(np.mean) | Find the average across all columns for every unique col1 group

Answer 52

df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean)

Question 53

Find the average across all columns for every unique col1 group

Answer 53

df.groupby(col1).agg(np.mean)

Question 54

Apply the function np.mean() across each column

Answer 54

df.apply(np.mean)

Question 55

Apply the function np.max() across each row

Answer 55

nf.apply(np.max,axis=1)

Question 56

Add the rows in df1 to the end of df2 (columns should be identical)

Answer 56

df1.append(df2)

Question 57

Add the columns in df1 to the end of df2 (rows should be identical)

Answer 57

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

Question 58

SQL-style join the columns in df1 with the columns on df2 where the rows for col have identical values. how can be one of ‘left’, ‘right’, ‘outer’, ‘inner’

Answer 58

df1.join(df2,on=col1,how=’inner’)

Question 59

Summary statistics for numerical columns

Answer 59

df.describe()

Question 60

Returns the mean of all columns

Answer 60

df.mean()

Question 61

Returns the correlation between columns in a DataFrame

Answer 61

df.corr()

Question 62

Returns the number of non-null values in each DataFrame column

Answer 62

df.count()

Question 63

Returns the highest value in each column

Answer 63

df.max()

Question 64

Returns the lowest value in each column

Answer 64

df.min()

Question 65

Returns the median of each column

Answer 65

df.median()

Question 66

Returns the standard deviation of each column

Answer 66

df.std()