Agregue varias columnas al marco de datos en Pandas

En Pandas, tenemos la libertad de agregar columnas en el marco de datos cuando sea necesario. Hay varias formas de agregar columnas al marco de datos de Pandas. 

Método 1: Agregar múltiples columnas a un marco de datos usando Listas

# importing pandas library
import pandas as pd
  
# creating and initializing a nested list
students = [['jackma', 34, 'Sydeny', 'Australia'],
            ['Ritika', 30, 'Delhi', 'India'],
            ['Vansh', 31, 'Delhi', 'India'],
            ['Nany', 32, 'Tokyo', 'Japan'],
            ['May', 16, 'New York', 'US'],
            ['Michael', 17, 'las vegas', 'US']]
  
# Create a DataFrame object
df = pd.DataFrame(students,
                  columns=['Name', 'Age', 'City', 'Country'],
                  index=['a', 'b', 'c', 'd', 'e', 'f'])
  
# Creating 2 lists 'marks' and 'gender'
marks = [85.4,94.9,55.2,100.0,40.5,33.5]
gender = ['M','F','M','F','F','M']
  
# adding lists as new column to dataframe df
df['Uni_Marks'] = marks
df['Gender'] = gender
  
# Displaying the Data frame
df

Producción :

Método 2: agregue varias columnas a un marco de datos utilizando   el método Dataframe.assign()

# importing pandas library
import pandas as pd
  
# creating and initializing a nested list
students = [['jackma', 34, 'Sydeny', 'Australia'],
            ['Ritika', 30, 'Delhi', 'India'],
            ['Vansh', 31, 'Delhi', 'India'],
            ['Nany', 32, 'Tokyo', 'Japan'],
            ['May', 16, 'New York', 'US'],
            ['Michael', 17, 'las vegas', 'US']]
  
# Create a DataFrame object
df = pd.DataFrame(students,
                  columns=['Name', 'Age', 'City', 'Country'],
                  index=['a', 'b', 'c', 'd', 'e', 'f'])
  
# creating columns 'Admissionnum' and 'Percentage'
# using dataframe.assign() function
df = df.assign(Admissionnum=[250, 800, 1200, 300, 400, 700], 
               Percentage=['85%', '90%', '75%', '35%', '60%', '80%'])
  
# Displaying the Data frame
df

Producción :

Método 3: agregue varias columnas a un marco de datos utilizando  el método Dataframe.insert()

# importing pandas library
import pandas as pd
  
# creating and initializing a nested list
students = [['jackma', 34, 'Sydeny', 'Australia'],
            ['Ritika', 30, 'Delhi', 'India'],
            ['Vansh', 31, 'Delhi', 'India'],
            ['Nany', 32, 'Tokyo', 'Japan'],
            ['May', 16, 'New York', 'US'],
            ['Michael', 17, 'las vegas', 'US']]
  
# Create a DataFrame object
df = pd.DataFrame(students,
                  columns=['Name', 'Age', 'City', 'Country'],
                  index=['a', 'b', 'c', 'd', 'e', 'f'])
  
# creating columns 'Age' and 'ID' at 
# 2nd and 3rd position using 
# dataframe.insert() function
df.insert(2, "Marks", [90, 70, 45, 33, 88, 77], True)
df.insert(3, "ID", [101, 201, 401, 303, 202, 111], True)
  
  
# Displaying the Data frame
df

Producción :

Método 4: Agregue múltiples columnas a un marco de datos usando   Dictionary y zip()

# importing pandas library
import pandas as pd
  
# creating and initializing a nested list
students = [['jackma', 34, 'Sydeny', 'Australia'],
            ['Ritika', 30, 'Delhi', 'India'],
            ['Vansh', 31, 'Delhi', 'India'],
            ['Nany', 32, 'Tokyo', 'Japan'],
            ['May', 16, 'New York', 'US'],
            ['Michael', 17, 'las vegas', 'US']]
  
# Create a DataFrame object
df = pd.DataFrame(students,
                  columns=['Name', 'Age', 'City', 'Country'],
                  index=['a', 'b', 'c', 'd', 'e', 'f'])
  
# creating 2 lists 'ids' and 'marks'
ids = [11, 12, 13, 14, 15, 16]
marks=[85,41,77,57,20,95,96]
  
# Creating columns 'ID' and 'Uni_marks'  
# using Dictionary and zip() 
df['ID'] = dict(zip(ids, df['Name']))
df['Uni_Marks'] = dict(zip(marks, df['Name']))
    
# Displaying the Data frame
df

Producción :