Pandas

Question 1

Drop Columns

Answer 1

df.drop(columns=[‘Column1’, ‘Column2’])

Question 2

Pivots

Answer 2

df.pivot(columns=’var’, values=’val’)

Question 3

Sort

Answer 3

df.sort_values(‘column1’)

Order rows by values of a column (low to high).

Question 4

Rename Columns

Answer 4

df.rename(columns = {‘y’:’year’})

Rename the columns of a DataFrame

Question 5

Head

Answer 5

df.head(n)

Select first n rows

Question 6

Tail

Answer 6

df.tail(n)

Select last n rows

Question 7

Using Query

Answer 7

query() allows Boolean expressions for filtering
rows.
df.query('Length > 7')
df.query('Length > 7 and Width < 8')
df.query('Name.str.startswith("abc")',
engine="python")

Question 8

Select rows 10-20.

Answer 8

df.iloc[10:20]

Question 9

Select columns in positions 1, 2 and 5 (first

column is 0).

Answer 9

df.iloc[:, [1, 2, 5]]

Question 10

Access single value by index

Answer 10

df.iat[1, 2]

Question 11

Access single value by label

Answer 11

df.at[4, ‘A’]

Question 12

Select rows meeting logical condition, and only the specific columns .

Answer 12

df.loc[df[‘a’] > 10, [‘a’, ‘c’]]

Question 13

Append rows of DataFrames

Answer 13

pd.concat([df1,df2])

Question 14

Append columns of DataFrames

Answer 14

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

Question 15

Gather columns into rows.

Answer 15

pd.melt(df)

Question 16

Logic in Python (and pandas)

Answer 16

< Less than 
!= Not equal to
> Greater than 
df.column.isin(values)  Group membership
== Equals 
pd.isnull(obj) Is NaN
<= Less than or equals pd.notnull(obj) Is not NaN
>= Greater than or equals &,|,~,^,df.any(),df.all() Logical and, or, not, xor, any, all

Question 17

Count number of rows with each unique value of variable

Answer 17

df[‘w’].value_counts()

Question 18

Tuple of # of rows, # of columns in DataFrame.

Answer 18

df.shape

Question 19

of distinct values in a column.

Answer 19

df[‘w’].nunique()

Question 20

Basic descriptive and statistics for each column (or GroupBy).

Answer 20

df.describe()

Question 21

Drop rows with any column having NA/null data.

Answer 21

df.dropna()

Question 22

Replace all NA/null data with value

Answer 22

df.fillna(value)

Question 23

Compute and append one or more new columns.

Answer 23

df.assign(Area=lambda df: df.Length*df.Height)

Question 24

Return a GroupBy object, grouped

by values in column named “col”

Answer 24

df.groupby(by=”col”)

size()
Size of each group.
agg(function)
Aggregate group using function

Question 25

Histogram for each column

Answer 25

df.plot.hist()

Question 26

Scatter chart using pairs of points

Answer 26

df.plot.scatter(x=’w’,y=’h’)

Question 27

Merge:

Join matching rows from bdf to adf.

Answer 27

pd.merge(adf, bdf,

how=’left’, on=’x1’)

Question 28

Merge:

Join matching rows from adf to bdf. (Right join)

Answer 28

pd.merge(adf, bdf,

how=’right’, on=’x1’)

Question 29

Merge:

Join data. Retain only rows in both sets.

Answer 29

pd.merge(adf, bdf,

how=’inner’, on=’x1’)

Question 30

Merge:

Join data. Retain all values, all rows.

Answer 30

pd.merge(adf, bdf,

how=’outer’, on=’x1’)

Question 31

Filtering Joins:

All rows in adf that have a match in bdf.

Answer 31

adf[adf.x1.isin(bdf.x1)]

Question 32

Filtering Joins:

All rows in adf that do not have a match in bdf.

Answer 32

adf[~adf.x1.isin(bdf.x1)]

Question 33

Set-like Operations:

Answer 33

pd.merge(ydf, zdf)
Rows that appear in both ydf and zdf
(Intersection).

pd.merge(ydf, zdf, how=’outer’)
Rows that appear in either or both ydf and zdf
(Union).

pd.merge(ydf, zdf, how='outer',
indicator=True)
.query('_merge == "left_only"')
.drop(columns=['_merge'])
Rows that appear in ydf but not zdf (Setdiff).

Question 34

Creating Dataframes:

Specify values for each column.

Answer 34

df = pd.DataFrame(
{"a" : [4, 5, 6],
"b" : [7, 8, 9],
"c" : [10, 11, 12]},
index = [1, 2, 3])

Question 35

Creating Dataframes:

Specify values for each row.

Answer 35

df = pd.DataFrame(
[[4, 7, 10],
[5, 8, 11],
[6, 9, 12]],
index=[1, 2, 3],
columns=['a', 'b', 'c'])

Question 36

Creating Dataframes:

Create DataFrame with a MultiIndex

Answer 36

df = pd.DataFrame(
{"a" : [4 ,5, 6],
"b" : [7, 8, 9],
"c" : [10, 11, 12]},
index = pd.MultiIndex.from_tuples(
[('d’, 1), ('d’, 2),
('e’, 2)], names=['n’, 'v']))

Question 37

Read csv with Pandas

Answer 37

pd.read_csv(“ruta.csv”, index =FALSE)

Question 38

Alternative way of creatina a Pivot table

Answer 38

pd.pivot_table(df, values= 0, index=[‘col 1’], columns=[‘col2’], aggfunc =np.sum)

Question 39

Save df as csv

Answer 39

df.to_csv(“filename”, index=False)

Question 40

Save as XLSX

Answer 40

with pd.ExcelWriter(“file_name”) as writer:

df. to_excel(writer,sheet_name=“name”,index =false)
df2. to_excel(writer, sheet_name=“name2”, index=false)