Data Wrangling in Python – Dropping Rows And Columns In pandas Dataframe

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Dropping Rows And Columns In pandas Dataframe

Import modules


import pandas as pd

Create a dataframe

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop an observation (row)

df.drop(['Cochice', 'Pima'])
name reports year
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop a variable (column)

Note: axis=1 denotes that we are referring to a column, not a row

df.drop('reports', axis=1)
name year
Cochice Jason 2012
Pima Molly 2012
Santa Cruz Tina 2013
Maricopa Jake 2014
Yuma Amy 2014

Drop a row if it contains a certain value (in this case, “Tina”)

Specifically: Create a new dataframe called df that includes all rows where the value of a cell in the name column does not equal “Tina”

df[df.name != 'Tina']
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop a row by row number (in this case, row 3)

Note that Pandas uses zero based numbering, so 0 is the first row, 1 is the second row, etc.

df.drop(df.index[2])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Maricopa Jake 2 2014
Yuma Amy 3 2014

can be extended to dropping a range

df.drop(df.index[[2,3]])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Yuma Amy 3 2014

or dropping relative to the end of the DF.

df.drop(df.index[-2])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Yuma Amy 3 2014

you can select ranges relative to the top or drop relative to the bottom of the DF as well.

df[:3] #keep top 3
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
df[:-3] #drop bottom 3 
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012

 

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