pandas Operations part2

Python Pandas

pandas user define functions

pandas Operations part2

astype

using astype we can convert one type to another.

Code :

Data = {'Name': ['GeeksForGeeks','Python'],
 'Unique ID': ['900','450'],
 'distance': ['40.2','30.5']}
 df = pd.DataFrame(Data)
 df['Unique ID'] = df['Unique ID'].astype(int)
 df['distance'] = df['distance'].astype(float)
 df.dtypes
 #df

where

using where , we can Filter the data.

Code :

 df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
 df['color'] = np.where(df['Set']=='Z', 'green', 'red')
 print(df)

replace

using replace, we can replace from source value to destination.

Code :

dk=pd.DataFrame({"BrandName":['A','B','ABC','D','AB'],"Specialty":[
 print(dk)
 print(dk.BrandName.replace(to_replace=['ABC','AB'],value=['A','B'])

drop_duplicates

using drop_duplicates , we are dropping the data.

Code :

import pandas as pd
 emp = {"Name": ["Parker", "Smith", "William", "Parker"],
 "Age": [21, 32, 29, 21]}
 #info = pd.DataFrame(emp)
 info = info.drop_duplicates()
 #print(info)
 info

sort_index

it will sort using index.

Code :

#now let us create our own datasets and perform the operations
 students = [ ('Jack', 34, 'Sydney') ,
             ('Riti', 31, 'Delhi' ) ,
             ('Aadi', 16, 'New York') ,
             ('Riti', 32, 'Delhi' ) ,
             ('Riti', 33, 'Delhi' ) ,
             ('Riti', 35, 'Mumbai' ),
             ('Ajay', 21, 'Hyderabad')
             ]
 #let us perform the sort by index
 df3.sort_index() # sorted by index only
 df3.sort_index(axis=0, ascending=False)
 #axis = 0 is by row

value_counts

return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

Code :

#df3['Name'].value_counts()
 df3['Name'].value_counts()