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pandas.DataFrame.append() takes a DataFrame as input and merges its rows with the rows of the calling DataFrame, returning a new DataFrame. If any columns in the input DataFrame do not exist in the calling DataFrame, then those columns will be added to the DataFrame and the missing values ​​will be set to NaN.

pandas.DataFrame.append()Method Syntax

DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)

parameter

Example Code: pandas.DataFrame.append()Add two DataFrames using

import pandas as pd

names_1=['Hisila', 'Brian','Zeppy']
salary_1=[23,30,21]

names_2=['Ram','Shyam',"Hari"]
salary_2=[22,23,31]

df_1 = pd.DataFrame({'Name': names_1, 'Salary': salary_1})
df_2 = pd.DataFrame({'Name': names_2, 'Salary': salary_2})

merged_df = df_1.append(df_2)
print(merged_df)

Output:

     Name  Salary
0  Hisila      23
1   Brian      30
2   Zeppy      21
    Name  Salary
0    Ram      22
1  Shyam      23
2   Hari      31
     Name  Salary
0  Hisila      23
1   Brian      30
2   Zeppy      21
0     Ram      22
1   Shyam      23
2    Hari      31

It df_1adds at the end of df_2, and returns merged_df, the rows of the two DataFrames merged. Here, merged_dfthe indices of are the same as their parent DataFrames.

Example Code: pandas.DataFrame.append()Append a DataFrame and Ignore the Index Using

import pandas as pd

names_1=['Hisila', 'Brian','Zeppy']
salary_1=[23,30,21]

names_2=['Ram','Shyam',"Hari"]
salary_2=[22,23,31]

df_1 = pd.DataFrame({'Name': names_1, 'Salary': salary_1})
df_2 = pd.DataFrame({'Name': names_2, 'Salary': salary_2})

merged_df = df_1.append(df_2,ignore_index=True)

print(df_1)
print(df_2)
print( merged_df)

Output:

     Name  Salary
0  Hisila      23
1   Brian      30
2   Zeppy      21
    Name  Salary
0    Ram      22
1  Shyam      23
2   Hari      31
     Name  Salary
0  Hisila      23
1   Brian      30
2   Zeppy      21
3     Ram      22
4   Shyam      23
5    Hari      31

It df_2appends df_1to the end of , where a new index is obtained merged_dfby using the parameter append()in the method .ignore_index=True

DataFrame.append()Set in methodverify_integrity=True

If we append()set it in the method verify_integrity=True, we will get duplicate indexes ValueError.

import pandas as pd

names_1=['Hisila', 'Brian','Zeppy']
salary_1=[23,30,21]

names_2=['Ram','Shyam',"Hari"]
salary_2=[22,23,31]

df_1 = pd.DataFrame({'Name': names_1, 'Salary': salary_1})
df_2 = pd.DataFrame({'Name': names_2, 'Salary': salary_2})

merged_df = df_1.append(df_2,verify_integrity=True)

print(df_1)
print(df_2)
print( merged_df)

Output:

ValueError: Indexes have overlapping values: Int64Index([0, 1, 2], dtype='int64')

Since the elements df_1in and df_2have the same index by default, this results in ValueError. To prevent this error, we use verify_integritythe default value of , which is verify_integrity=False.

Example Code: Adding DataFrame with Different Columns

If we append a different column DataFrame, this column is added to the generated DataFrame, and the corresponding cells of the column that do not exist in the original DataFrameor other are set to .DataFrameNaN

import pandas as pd

names_1=['Hisila', 'Brian','Zeppy']
salary_1=[23,30,21]

names_2=['Ram','Shyam',"Hari"]
salary_2=[22,23,31]
Age=[30,31,33]

df_1 = pd.DataFrame({'Name': names_1, 'Salary': salary_1})
df_2 = pd.DataFrame({'Name': names_2, 'Salary': salary_2,"Age":Age})

merged_df = df_1.append(df_2, sort=False)

print(df_1)
print(df_2)
print( merged_df)

Output:

     Name  Salary
0  Hisila      23
1   Brian      30
2   Zeppy      21
    Name  Salary  Age
0    Ram      22   30
1  Shyam      23   31
2   Hari      31   33
     Name  Salary   Age
0  Hisila      23   NaN
1   Brian      30   NaN
2   Zeppy      21   NaN
0     Ram      22  30.0
1   Shyam      23  31.0
2    Hari      31  33.0

Here, df_1the rows of get the value Ageof the column NaNbecause Agethe column only exists in df_2.

We have also set it up sort=Falseto silence warnings about sort being deprecated in a future version of Pandas.