import pandas as pd

​

df = pd.DataFrame({'a':[1,2], 'b':[3,4]})

df['c'] = df.apply(lambda row: row.a + row.b, axis=1)

df

#    a  b  c

# 0  1  3  4

# 1  2  4  6

#using the insert function:

df.insert(location, column_name, list_of_values) 

#example

df.insert(0, 'new_column', ['a','b','c'])

#explanation:

#put "new_column" as first column of the dataframe

#and puts 'a','b' and 'c' as values

​

#using array access:

df['new_column_name'] = value

# pre 0.24

feature_file_df['RESULT'] = RESULT_df['RESULT'].values

# >= 0.24

feature_file_df['RESULT'] = RESULT_df['RESULT'].to_numpy()

# pre 0.24

feature_file_df['RESULT'] = RESULT_df['RESULT'].values

# >= 0.24

feature_file_df['RESULT'] = RESULT_df['RESULT'].to_numpy()

​

​

merge(left, right, how='left', on=None, left_on=None, right_on=None,

      left_index=False, right_index=False, sort=True,

      suffixes=('_x', '_y'), copy=True)

​

# import pandas library

import pandas as pd

​

# create pandas DataFrame

df = pd.DataFrame({'team': ['India', 'South Africa', 'New Zealand', 'England'],

                   'points': [10, 8, 3, 5],

                   'runrate': [0.5, 1.4, 2, -0.6],

                   'wins': [5, 4, 2, 2]})

​

# print the DataFrame

print(df)

​

​

# insert the new column at the specific position

df.insert(3, "lost", [2, 1, 3, 4], True)

​

# Print the new DataFrame

print(df)

# Import pandas package

import pandas as pd

# Define a dictionary containing Students data

data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],

        'Height': [5.1, 6.2, 5.1, 5.2],

        'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}

# Convert the dictionary into DataFrame

df = pd.DataFrame(data)

# Declare a list that is to be converted into a column

address = ['Delhi', 'Bangalore', 'Chennai', 'Patna']

# Using 'Address' as the column name

# and equating it to the list

df['Address'] = address

# Observe the result

print(df)

Other['Extracurr'] = count