Drop or delete the row in python pandas with conditions

In this tutorial we will learn how to drop or delete the row in python pandas by index, delete row by condition in python pandas and drop rows by position. Dropping a row in pandas is achieved by using .drop() function. Lets see example of each.

  • Drop Rows with Duplicate in pandas.
  • Delete or Drop rows with condition in python pandas using drop() function.
  • Drop rows by index / position in pandas.
  • Drop NA rows or missing rows in pandas python.

 

Syntax of drop() function in pandas :

DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)
  • labels: String or list of strings referring row.
  • axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns.
  • index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together.
  • level: Used to specify level, in case data frame is having multiple level index.
  • inplace: Makes changes in original Data Frame if True.
  • errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’

 

Create Dataframe:

import pandas as pd
import numpy as np

#Create a DataFrame

import pandas as pd
import numpy as np

d = { 'Name':['Alisa','raghu','jodha','jodha','raghu','Cathrine', 'Alisa','Bobby','Bobby','Alisa','raghu','Cathrine'],
     'Age':[26,23,23,23,23,24,26,24,22,26,23,24], 
     'Score':[85,31,55,55,31,77,85,63,42,85,31,np.nan]}
df = pd.DataFrame(d,columns=['Name','Age','Score'])
df

the dataframe will be

Drop rows in pandas python drop() 1

 

Simply drop a row or observation:

Dropping the second and third row of a dataframe is achieved as follows

# Drop an observation or row
df.drop([1,2])

The above code will drop the second and third row.
0 – represents 1st row
1- represnts 2nd row and so on. So the resultant dataframe will be

Drop rows in pandas python drop() 2

 

Drop a row or observation by condition:

we can drop a row when it satisfies a specific condition

# Drop a row by condition
df[df.Name != 'Alisa']

The above code takes up all the names except Alisa, thereby dropping the row with name ‘Alisa’. So the resultant dataframe will be

Drop rows in pandas python drop() 3

 

Drop a row or observation by index:

We can drop a row by index as shown below

# Drop a row by index
df.drop(df.index[2])

The above code drops the row with index number 2. So the resultant dataframe will be

Drop rows in pandas python drop() 4

 

Drop the row by position:

Now let’s drop the bottom 3 rows of a dataframe as shown below

# Drop bottom 3 rows
df[:-3]

The above code selects all the rows except bottom 3 rows, there by dropping bottom 3 rows, so the resultant dataframe will be

Drop rows in pandas python drop() 5

 

Drop Duplicate rows of the dataframe in pandas

Drop duplicates in pandas python 1

now lets simply drop the duplicate rows in pandas as shown below

# drop duplicate rows

df.drop_duplicates()

In the above example first occurrence of the duplicate row is kept and subsequent  duplicate occurrence will be deleted, so the output will be

Drop duplicates in pandas python 7

For further detail on drop duplicates one can refer our page on Drop duplicate rows in pandas python drop_duplicates()

 

Drop rows with NA values in pandas python

Drop the rows even with single NaN or single missing values.

df.dropna()

so the resultant table on which rows with NA values dropped will be

Outputs:

For further detail on drop rows with NA values one can refer our page

 


Other related topics :

for documentation on drop() function kindly refer here

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