Pandas

Question 1

To merge csv tables from scrape in Pandas

Answer 1

DataframeName = pd.concat([df, df2, df3], axis=1, sort=False)

Question 2

To find if there are duplicate rows

Answer 2

df.column name.duplicated()

Question 3

Displays the duplicate rows

Answer 3

df.loc[df.duplicated(), :]

Question 4

To mark duplicates except for the first occurrence

Answer 4

df.loc[df.duplicated(keep = ‘first’), :]

Question 5

To mark duplicates except for the last occurrence

Answer 5

df.loc[df.duplicated(keep = ‘last’), :]

Question 6

To mark all duplicates as True (all will be displayed)

Answer 6

df.loc[df.duplicated(keep = False), :]

Question 7

To drop duplicates from the data frame

Answer 7

df.drop_duplicates(keep=’first’).shape

Question 8

Save file in Numpy

Load file in Numpy

Answer 8

Arr = np.arrange(10)

np. save(‘file_name’, arr)
np. load(‘file_name.npy’)

Question 9

Drop row(s)

Answer 9

DataframeName.drop([‘row name’, ‘row name’])

Question 10

Transpose Datafram (swap rows and columns)

Answer 10

DataframeName.T

Question 11

Add 2 dfs, and keep values where rows and columns dont match.

Answer 11

df1.add(df2, fill_value = 0)

Question 12

Creating DF with 12 count, 4 rows, 3 columns, A, B,C as column nanes, and 4 states as index.

Answer 12

df = pd.Dataframe(np.arrange(12.).reshape ((4,3), columns = list (‘ABC’), index = [‘New York’, ‘Florida’, ‘California’, ‘Nevada’])

Question 13

Create own dataframe

Answer 13

df = pd.dataframe({‘A’ : [0, 1, 2, 3, 4], ‘B’: [5, 6, 7, 8], ‘C’: [9, 10, 11, 12]})

Question 14

Load csv into Pandas and create header row

Answer 14

“pd.read_csv(‘examples/ex2.csv’, names=[‘a’, ‘b’, ‘c’, ‘d’, ‘message’])”

Question 15

To check for duplicate rows

Answer 15

data.duplicated()

Question 16

To drop duplicates

Answer 16

data.drop_duplicates()

Question 17

Add mean/average column for averages

Answer 17

DataframeName[‘Mean’] = DataframeName.mean(numeric_only=True, axis =1)

Question 18

Surpressing scientific notation

Answer 18

pd.set_option(‘display.float_format’, ‘‘.format)

Question 19

Supress scientific notation and format with dollar sign and commas

Answer 19

pd.set_option(‘display.float_format’, ‘${:,.2f}’.format)

Question 20

To view rows or select specific row and all columns

Answer 20

DataFrameName.loc[‘Row_Name’, :]
loc.[what rows do i want, what column do I want]
: = all columns

Question 21

To view multiple rows

Answer 21

DataFrameName.loc[[‘Row_Name’, ‘Row_Name’, ‘Row_Name’ :]]

Question 22

To select a column and all rows

Answer 22

df9.loc[:, ‘column_name’ ]

Question 23

To select multiple columns and all rows

Answer 23

df9.loc[:, [‘column_name.’, ‘column_name’]]

Question 24

To arrange list into array

Answer 24

ListName.reshape((#of rows, # of columns))