Get all rows in a Pandas DataFrame containing a given substring

Python Methods and Functions | String Variables

Code # 1: Check the PG values ​​in the Position column

# pandas import

import pandas as pd

 
# Create a data frame specifying lists

df = pd.DataFrame ({ `Name` : [ `Geeks` , ` Peter` , ` James` , `Jack` , `Lisa` ],

`Team` : [ `Boston` , ` Boston` , `Boston` , `Chele` , ` Barse` ],

`Position` : [ `PG` , ` PG` , `UG` , ` PG ` , ` UG` ],

`Number` : [ 3 , 4 , 7 , 11 , 5 ],

  `Age` : [ 33 , 25 , 34 , 35 , 28 ],

`Height` : [ `6-2` , ` 6 -4` , `5-9` , `6-1` , ` 5-8` ],

`Weight` : [ 89 , 79 , 113 , 78 , 84 ],

  `College` : [ ` MIT` , ` MIT` , `MIT` , `Stanford` , `Stanford` ],

  ` Salary` : [ 99999 , 99994 , 89999 , 78889 , 87779 ]},

index = [ `ind1` , `ind2` , ` ind3` , ` ind4` , `ind5` ])

print (df, "" )

 

print ( "Check PG values in Position column: " )

df1 = df [ `Position` ]. str . contains ( "PG" )

print (df1)

Output:

But this result doesn`t seem very useful as it returns bool values ​​with an index. Let`s see if we can do something better.

Code # 2: Getting rows that match a condition

# import pandas as pd

import pandas as pd

 
# Create a data frame specifying lists

df = pd.DataFrame ({ `Name` : [ `Geeks` , `Peter` , ` James` , `Jack` , ` Lisa` ],

  ` Team` : [ `Boston` , `Boston` , `Boston` , ` Chele` , `Barse` ],

  `Position` : [ ` PG` , `PG` , ` UG` , `PG` , ` UG` ],

`Number` : [ 3 , 4 , 7 , 11 , 5 ],

`Age` : [ 33 , 25 , 34 , 35 , 28 ],

` Height` : [ `6-2` , `6-4` , `5-9` , ` 6-1` , `5-8` ],

  `Weight` : [ 89 , 79 , 113 , 78 , 84 ],

  ` College` : [ ` MIT` , `MIT` , `MIT` , `Stanford` , ` Stanford` ],

`Salary` : [ 99999 , 99994 , 89999 , 78889 , 87779 ]},

  index = [ ` ind1` , `ind2` , `ind3` , ` ind4` , `ind5` ])

  

df1 = df [df [ `Position` ]. str . contains ( " PG " )]

print (df1)

Output:

Code # 3: Filter out all lines where either command contains" Boston "or college contains" MIT ".

# pandas import

import pandas as pd

 
# Create a data frame specifying lists

df = pd.DataFrame ({ `Name` : [ `Geeks` , ` Peter` , `James` , ` Jack` , `Lisa` ],

`Team` : [ ` Boston` , `Boston` , ` Boston` , `Chele` , `Barse` ],

  `Position` : [ ` PG` , `PG` , ` UG` , `PG` , `UG` ],

  `Number` : [ 3 , 4 , 7 , 11 , 5 ],

`Age` : [ 33 , 25 , 34 , 35 , 28 ],

  ` Height` : [ `6-2` , `6-4` , `5-9` , ` 6-1` , `5-8` ],

  ` Weight` : [ 89 , 79 , 113 , 78 , 84 ],

`College` : [ `MIT` , ` MIT` , `MIT` , `Stanford` , ` Stanford` ],

`Salary` : [ 99999 , 99994 , 89999 , 78889 , 87779 ]},

index = [ `ind1` , `ind2` , ` ind3` , `ind4` , `ind5` ])

 

 

df1 = df [df [ `Team` ]. str . contains ( " Boston " ) | df [ `College` ]. str . contains ( `MIT` )]

print (df1)

Output:

Code # 4: Check filter strings. The team name contains "Boston and the Position must be PG.

# pandas module import

import pandas as pd 

  
# create data frame

df = pd.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv "

 

 

df1 = df [df [ `Team` ]. str . contains ( `Boston` ) & amp; df [ `Position` ]. str . contains ( ` PG` )]

df1

Output:

Code # 5: Checking filter strings Position contains PG, and College should contain UC.

# pandas module import

import pandas as pd 

 
# create data frame

df = pd.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv "

 

 

df1 = df [df [ `Position` ]. str . Contains ( "PG" ) & amp; df [ `College` ]. str . contains ( `UC` )]

df1

Exit :





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