To append two Pandas DataFrames, you can use the append() function. There are multiple ways to append two pandas DataFrames, In this article, I will explain how to append two or more pandas DataFrames by using several functions.
In order to append two DataFrames you can use DataFrame.append() function. When you are appending two or more DataFrames, pass all DataFrames as a list to this method.
Key Points –
- Use the
append()function to concatenate two DataFrames vertically, adding rows from one DataFrame to the end of the other. - Specify the
ignore_index=Trueparameter to reset the index of the resulting DataFrame after appending, ensuring a continuous index. - Consider alternatives such as
pd.concat()for more complex concatenation operations orDataFrame.append()for appending a single row or column. - The
append()method also appends DataFrames but is deprecated in recent pandas versions. - You can append multiple DataFrames by passing a list of DataFrames to
pd.concat().
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Quick Examples of Appending
Below are some quick examples of appending two DataFrames in pandas.
# Quick examples of append two dataframes
# Append two DataFrames of same column
df3 = df1.append(df2)
# Append two DataFrames of different columns
df2 = df.append(df1)
# Using append() with ignore_index
df2 = df.append(df1, ignore_index=True)
# Appending three DataFrames
df3 = df.append([df1, df2], ignore_index=True)
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Usage of Append Two Pandas DataFrames
Appending two DataFrames means combining them by stacking one DataFrame’s rows below the other’s, effectively increasing the total number of rows while keeping the columns aligned. It’s like adding more records to an existing table.
To run some examples of appending two pandas DataFrames, let’s create DataFrame using data from a dictionary.
# Create two DataFrames with same columns
import pandas as pd
df1 = pd.DataFrame({'Courses': ["Spark","PySpark","Python","pandas"],
'Fee' : [20000,25000,22000,24000]})
print("First DataFrame:\n", df1)
df2 = pd.DataFrame({'Courses': ["Pandas","Hadoop","Hyperion","Java"],
'Fee': [25000,25200,24500,24900],
'Duration': ['30days','35days','40days','45days']})
print("Second DataFrame:\n", df2)
Yields below output.
To append two DataFrames with the same columns in Pandas, you can utilize the append() function. This function concatenates the DataFrames along the specified axis, filling in NaN values for rows where columns don't match.
# Append two DataFrames of same columns
# using append() function
df3 = df1.append(df2)
print("After appending DataFrames:\n", df3)
Yields below output.
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Append Two DataFrames With the Different Columns
Alternatively, to append two pandas DataFrames with different columns, we can utilize the append() method. This method allows you to combine DataFrames along a specified axis (rows or columns), and it handles the alignment of columns with different names.
# Craete DataFrames of different columns
import pandas as pd
df1 = pd.DataFrame({'Courses': ["Spark","PySpark","Python","pandas"],
'Fee' : [20000,25000,22000,24000]})
print("First DataFrame:\n", df1)
df2 = pd.DataFrame({'discount': [2500,2520,2450,2490],
'Duration': ['30days','35days','40days','45days']})
print("Second DataFrame:\n", df2)
# Append two DataFrames of different columns
# using append() function
df3 = df1.append(df2)
print("After appending DataFrames:\n", df3)
Yields below output.
# Output:
First DataFrame:
Courses Fee
0 Spark 20000
1 PySpark 25000
2 Python 22000
3 pandas 24000
Second DataFrame:
discount Duration
0 2500 30days
1 2520 35days
2 2450 40days
3 2490 45days
After appending DataFrames:
Courses Fee discount Duration
0 Spark 20000.0 NaN NaN
1 PySpark 25000.0 NaN NaN
2 Python 22000.0 NaN NaN
3 pandas 24000.0 NaN NaN
0 NaN NaN 2500.0 30days
1 NaN NaN 2520.0 35days
2 NaN NaN 2450.0 40days
3 NaN NaN 2490.0 45days
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Append Two DataFrames Ignore Index
To append two Pandas DataFrames while ignoring the index, you can use the ignore_index=True parameter with the append() function.
# Using append() with ignore_index
df3 = df1.append(df2, ignore_index=True)
print(df3)
Yields below output.
# Output:
Courses Fee discount Duration
0 Spark 20000.0 NaN NaN
1 PySpark 25000.0 NaN NaN
2 Python 22000.0 NaN NaN
3 pandas 24000.0 NaN NaN
4 NaN NaN 2500.0 30days
5 NaN NaN 2520.0 35days
6 NaN NaN 2450.0 40days
7 NaN NaN 2490.0 45days
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Append Three DataFrames
Similarly, If you have three DataFrames pass all these as a list to the append() method. Using ignore_index=True allows you to reset a DataFrame's index of a Pandas DataFrame to start from zero.
# Create third DataFrame
df2 = pd.DataFrame({'Courses':['PHP','GO'],
'Duration':['30day','40days'],
'Fee':[10000,23000]})
# Appending multiple DataFrame
df3 = df.append([df1, df2], ignore_index=True)
print(df3)
Yields below output
# Output:
Courses Fee Duration
Courses Fee Duration
0 Spark 20000 NaN
1 PySpark 25000 NaN
2 Python 22000 NaN
3 pandas 24000 NaN
4 Pandas 25000 30days
5 Hadoop 25200 35days
6 Hyperion 24500 40days
7 Java 24900 45days
8 PHP 10000 30day
9 GO 23000 40days
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Complete Example of Append Two DataFrames
import pandas as pd
# Complete Example of Append Two DataFrames
df = pd.DataFrame({'Courses': ["Spark","PySpark","Python","pandas"],
'Fee' : [20000,25000,22000,24000]})
df1 = pd.DataFrame({'Courses': ["Pandas","Hadoop","Hyperion","Java"],
'Fee': [25000,25200,24500,24900],
'Duration': ['30days','35days','40days','45days']})
# Using append() method
df3 = df1.append(df2)
print(df3)
# Using append() with ignore_index
df3 = df1.append(df2, ignore_index=True)
print(df2)
# Create third DataFrame
df2 = pd.DataFrame({'Courses':['PHP','GO'],
'Duration':['30day','40days'],
'Fee':[10000,23000]})
# Appending multiple DataFrame
df3 = df.append([df1, df2], ignore_index=True)
print(df3)
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FAQ on Append Two DataFrames
You can use the append() function in Pandas to concatenate two DataFrames vertically. Simply call the append() method on one DataFrame and pass the other DataFrame as an argument.
You can use the ignore_index=True parameter when appending the DataFrames to reset the index of the resulting DataFrame, starting from zero.
The DataFrames do not necessarily need to have the same columns. If they have different columns, the missing columns will be filled with NaN values.
Appending DataFrames (using pd.concat()) stacks DataFrames vertically, combining rows. Merging DataFrames (using pd.merge()) combines DataFrames based on common columns, aligning rows based on common values in those columns.
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Conclusion
In this article, I have explained appending two Pandas DataFrames by examples. Also learned how to append even three DataFrame by passing all DF's you wanted to append as a list.
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