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Get all rows in a Pandas DataFrame containing a given substring

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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

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