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Python | Pandas Merging, Joining and Concatenating

Last Updated : 14 Jun, 2025
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Pandas DataFrame helps for working with data organized in rows and columns. When we're working with multiple datasets we need to combine them in different ways. Pandas provides three simple methods like merging, joining and concatenating. These methods help us to combine data in various ways whether it's matching columns, using indexes or stacking data on top of each other. In this article, we'll see these methods.

Concatenating DataFrames

Concatenating DataFrames means combining them either by stacking them on top of each other (vertically) or placing them side by side (horizontally). In order to Concatenate dataframe, we use different methods which are as follows:

1. Concatenating DataFrame using .concat()

To concatenate DataFrames, we use the pd.concat() function. This function allows us to combine multiple DataFrames into one by specifying the axis (rows or columns).

Here we will be loading and printing the custom dataset, then we will perform the concatenation using pd.concat().

Python 
import pandas as pd  data1 = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],          'Age': [27, 24, 22, 32],          'Address': ['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],          'Qualification': ['Msc', 'MA', 'MCA', 'Phd']}  data2 = {'Name': ['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],          'Age': [17, 14, 12, 52],          'Address': ['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],          'Qualification': ['Btech', 'B.A', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1, index=[0, 1, 2, 3])  df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])  print(df, "\n\n", df1)

Output:

z1
output

Now we apply .concat function in order to concat two dataframe.

Python 
frames = [df, df1]  res1 = pd.concat(frames) res1

Output:

programing_14
output

2. Concatenating DataFrames by Setting Logic on Axes

We can modify the concatenation by setting logic on the axes. Specifically we can choose whether to take the Union (join='outer') or Intersection (join='inner') of columns.

Python 
import pandas as pd  data1 = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],          'Age': [27, 24, 22, 32],          'Address': ['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],          'Qualification': ['Msc', 'MA', 'MCA', 'Phd'],          'Mobile No': [97, 91, 58, 76]}  data2 = {'Name': ['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'],          'Age': [22, 32, 12, 52],          'Address': ['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],          'Qualification': ['MCA', 'Phd', 'Bcom', 'B.hons'],          'Salary': [1000, 2000, 3000, 4000]}  df = pd.DataFrame(data1, index=[0, 1, 2, 3])  df1 = pd.DataFrame(data2, index=[2, 3, 6, 7])  print(df, "\n\n", df1)

Output:

z3
output

Now we set axes join = inner for intersection of dataframe which keeps only the common columns.

Python 
res2 = pd.concat([df, df1], axis=1, join='inner')  res2

Output:

z4
output

Now we set axes join = outer for union of dataframe which keeps all columns from both DataFrames.

Python 
res2 = pd.concat([df, df1], axis=1, sort=False)  res2

Output:

z5
output

3. Concatenating DataFrames by Ignoring Indexes

Sometimes the indexes of the original DataFrames may not be relevant. We can ignore the indexes and reset them using the ignore_index argument. This is useful when we don't want to carry over any index information.

Python 
import pandas as pd     data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],          'Age':[27, 24, 22, 32],          'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],          'Qualification':['Msc', 'MA', 'MCA', 'Phd'],         'Mobile No': [97, 91, 58, 76]}   data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'],          'Age':[22, 32, 12, 52],          'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],          'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],         'Salary':[1000, 2000, 3000, 4000]}     df = pd.DataFrame(data1,index=[0, 1, 2, 3])    df1 = pd.DataFrame(data2, index=[2, 3, 6, 7])        print(df, "\n\n", df1)

Output:

z6
output

Now we are going to apply ignore_index as an argument.

Python 
res = pd.concat([df, df1], ignore_index=True)   res

Output:

z7
output

4. Concatenating DataFrame with group keys :

If we want to retain information about the DataFrame from which each row came, we can use the keys argument. This assigns a label to each group of rows based on the source DataFrame.

Python 
import pandas as pd  data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Msc', 'MA', 'MCA', 'Phd']}  data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],         'Age':[17, 14, 12, 52],         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1,index=[0, 1, 2, 3])  df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])  print(df, "\n\n", df1)

Output:

z8
output

Here we will use keys as an argument. The keys argument creates a hierarchical index where each row is labeled with the source DataFrame (df1 or df2).

Python 
frames = [df, df1 ]  res = pd.concat(frames, keys=['x', 'y']) res

Output:

z9
output

5. Concatenating Mixed DataFrames and Series

We can also concatenate a mix of Series and DataFrames. If we include a Series in the list, it will automatically be converted to a DataFrame and we can specify the column name.

Python 
import pandas as pd  data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Msc', 'MA', 'MCA', 'Phd']}  df = pd.DataFrame(data1,index=[0, 1, 2, 3])  s1 = pd.Series([1000, 2000, 3000, 4000], name='Salary')  print(df, "\n\n", s1)

Output:

z10
output

Here we are going to mix Series and dataframe together.

Python 
res = pd.concat([df, s1], axis=1)  res

Output:

z11
output

Merging DataFrame

Merging DataFrames in Pandas is similar to performing SQL joins. It is useful when we need to combine two DataFrames based on a common column or index. The merge() function provides flexibility for different types of joins.

There are four basic ways to handle the join (inner, left, right and outer) depending on which rows must retain their data.
1. Merging DataFrames Using One Key

We can merge DataFrames based on a common column by using the on argument. This allows us to combine the DataFrames where values in a specific column match.

Python 
import pandas as pd  data1 = {'key': ['K0', 'K1', 'K2', 'K3'],          'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],}  data2 = {'key': ['K0', 'K1', 'K2', 'K3'],          'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1)  df1 = pd.DataFrame(data2)   print(df, "\n\n", df1)

Output:

z12
output

Now here we are using .merge() with one unique key combination.

Python 
res = pd.merge(df, df1, on='key')  res

Output:

z13
output

2. Merging DataFrames Using Multiple Keys

We can also merge DataFrames based on more than one column by passing a list of column names to the on argument.

Python 
import pandas as pd  data1 = {'key': ['K0', 'K1', 'K2', 'K3'],          'key1': ['K0', 'K1', 'K0', 'K1'],          'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],}  data2 = {'key': ['K0', 'K1', 'K2', 'K3'],          'key1': ['K0', 'K0', 'K0', 'K0'],          'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1)  df1 = pd.DataFrame(data2)   print(df, "\n\n", df1)

Output:

z14
output

Now we merge dataframe using multiple keys.

Python 
res1 = pd.merge(df, df1, on=['key', 'key1'])  res1

Output:

z15
output

3. Merging DataFrames Using the how Argument

We use how argument to merge specifies how to find which keys are to be included in the resulting table. If a key combination does not appear in either the left or right tables, the values in the joined table will be NA. Here is a summary of the how options and their SQL equivalent names:

MERGE METHODJOIN NAMEDESCRIPTION
leftLEFT OUTER JOINUse keys from left frame only
rightRIGHT OUTER JOINUse keys from right frame only
outerFULL OUTER JOINUse union of keys from both frames
innerINNER JOINUse intersection of keys from both frames
Python 
import pandas as pd  data1 = {'key': ['K0', 'K1', 'K2', 'K3'],          'key1': ['K0', 'K1', 'K0', 'K1'],          'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],}  data2 = {'key': ['K0', 'K1', 'K2', 'K3'],          'key1': ['K0', 'K0', 'K0', 'K0'],          'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],         'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1)  df1 = pd.DataFrame(data2)   print(df, "\n\n", df1)

Output:

z16
output

Now we set how = 'left' in order to use keys from left frame only. In this it includes all rows from the left DataFrame and only matching rows from the right.

Python 
res = pd.merge(df, df1, how='left', on=['key', 'key1'])  res

Output:

z17
output

 Now we set how = 'right' in order to use keys from right frame only. In this it includes all rows from the right DataFrame and only matching rows from the left.

Python 
res1 = pd.merge(df, df1, how='right', on=['key', 'key1'])  res1

Output:

z18
output

 Now we set how = 'outer' in order to get union of keys from dataframes. In this it combines all rows from both DataFrames, filling missing values with NaN.

Python 
res2 = pd.merge(df, df1, how='outer', on=['key', 'key1'])  res2

Output:

z19
output

 Now we set how = 'inner' in order to get intersection of keys from dataframes. In this it only includes rows where there is a match in both DataFrames.

Python 
res3 = pd.merge(df, df1, how='inner', on=['key', 'key1'])  res3

Output:

z20
output

Joining DataFrame

The .join() method in Pandas is used to combine columns of two DataFrames based on their indexes. It's a simple way of merging two DataFrames when the relationship between them is primarily based on their row indexes. It is used when we want to combine DataFrames along their indexes rather than specific columns.

1. Joining DataFrames Using .join()

If both DataFrames have the same index, we can use the .join() function to combine their columns. This method is useful when we want to merge DataFrames based on their row indexes rather than columns.

Python 
import pandas as pd   data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32]}  data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],         'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1,index=['K0', 'K1', 'K2', 'K3'])  df1 = pd.DataFrame(data2, index=['K0', 'K2', 'K3', 'K4'])   print(df, "\n\n", df1)

Output:

z21
output

Now we are using .join() method in order to join dataframes

Python 
res = df.join(df1)  res

Output:

z22
output

 Now we use how = 'outer' in order to get union

Python 
res1 = df.join(df1, how='outer')  res1

Output:

z23
output

2. Joining DataFrames Using the "on" Argument

If we want to join DataFrames based on a column (rather than the index), we can use the on argument. This allows us to specify which column(s) should be used to align the two DataFrames.

Python 
import pandas as pd  data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],         'Age':[27, 24, 22, 32],         'Key':['K0', 'K1', 'K2', 'K3']}  data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],         'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1)  df1 = pd.DataFrame(data2, index=['K0', 'K2', 'K3', 'K4'])   print(df, "\n\n", df1)

Output:

z24
output

Now we are using .join with “on” argument.

Python 
res2 = df.join(df1, on='Key')  res2

Output:

z25
output

3. Joining DataFrames with Different Index Levels (Multi-Index)

In some cases, we may be working with DataFrames that have multi-level indexes. The .join() function also supports joining DataFrames that have different index levels by specifying the index levels.

Python 
import pandas as pd  data1 = {'Name':['Jai', 'Princi', 'Gaurav'],         'Age':[27, 24, 22]}  data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kanpur'],         'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']}  df = pd.DataFrame(data1, index=pd.Index(['K0', 'K1', 'K2'], name='key'))  index = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),                                    ('K2', 'Y2'), ('K2', 'Y3')],                                    names=['key', 'Y'])  df1 = pd.DataFrame(data2, index= index)   print(df, "\n\n", df1)

Output:

z26
output

Now we join singly indexed dataframe with multi-indexed dataframe.

Python 
result = df.join(df1, how='inner')  result

Output:

z27
output

By mastering the technique of concatenating, merging and joining DataFrames, we'll see the full potential of our data which makes it easier to manipulate, analyze and derive meaningful insights.


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    Adding rows to a Pandas DataFrame is a common task in data manipulation and can be achieved using methods like loc[], and concat(). Method 1. Using loc[] - By Specifying its Index and ValuesThe loc[] method is ideal for directly modifying an existing DataFrame, making it more memory-efficient compar
    4 min read

    Grouping Data

    Pandas GroupBy
    The groupby() function in Pandas is important for data analysis as it allows us to group data by one or more categories and then apply different functions to those groups. This technique is used for handling large datasets efficiently and performing operations like aggregation, transformation and fi
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    Grouping Rows in pandas
    Pandas is the most popular Python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python. Let's see how to group rows in Pandas Dataframe with help of multiple examples. Example 1: For grouping rows in Pandas, we wi
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    Combining Multiple Columns in Pandas groupby with Dictionary
    Combining multiple columns in Pandas groupby operation with a dictionary helps to aggregate and summarize the data in a custom manner. It is useful when you want to apply different aggregation functions to different columns of the same dataset. Let's take an example of a sales dataset, where we need
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    Merging, Joining, Concatenating and Comparing

    Python | Pandas Merging, Joining and Concatenating
    Pandas DataFrame helps for working with data organized in rows and columns. When we're working with multiple datasets we need to combine them in different ways. Pandas provides three simple methods like merging, joining and concatenating. These methods help us to combine data in various ways whether
    9 min read
    Python | Pandas Series.str.cat() to concatenate string
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.Pandas str.cat() is used to concatenate strings to the passed caller series of string.
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    Python - Pandas dataframe.append()
    Pandas append function is used to add rows of other dataframes to end of existing dataframe, returning a new dataframe object. Columns not in the original data frames are added as new columns and the new cells are populated with NaN value.Append Dataframe into another DataframeIn this example, we ar
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    Python | Pandas Series.append()
    Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.append() function is used to
    4 min read
    Pandas Index.append() - Python
    Index.append() method in Pandas is used to concatenate or append one Index object with another Index or a list/tuple of Index objects, returning a new Index object. It does not modify the original Index. Example:Pythonimport pandas as pd idx1 = pd.Index([1, 2, 3]) idx2 = pd.Index([4, 5]) res = idx1.
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    Python | Pandas Series.combine()
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Series.combine() is a series mathematical operation method. This is used to com
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    Add a row at top in pandas DataFrame
    Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we can add a row at top in pandas DataFrame.Observe this dataset first.  Python3 # importing pandas module import pandas as pd # making data fram
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    Python | Pandas str.join() to join string/list elements with passed delimiter
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas str.join() method is used to join all elements in list present in a series with
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    Join two text columns into a single column in Pandas
    Let's see the different methods to join two text columns into a single column. Method #1: Using cat() function We can also use different separators during join. e.g. -, _, " " etc. Python3 1== # importing pandas import pandas as pd df = pd.DataFrame({'Last': ['Gaitonde', 'Singh', 'Mathur'], 'First':
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    How To Compare Two Dataframes with Pandas compare?
    A DataFrame is a 2D structure composed of rows and columns, and where data is stored into a tubular form. It is mutable in terms of size, and heterogeneous tabular data. Arithmetic operations can also be performed on both row and column labels. To know more about the creation of Pandas DataFrame. He
    5 min read
    How to compare the elements of the two Pandas Series?
    Sometimes we need to compare pandas series to perform some comparative analysis. It is possible to compare two pandas Series with help of Relational operators, we can easily compare the corresponding elements of two series at a time. The result will be displayed in form of True or False. And we can
    3 min read

    Working with Date and Time

    Python | Working with date and time using Pandas
    While working with data, encountering time series data is very usual. Pandas is a very useful tool while working with time series data.  Pandas provide a different set of tools using which we can perform all the necessary tasks on date-time data. Let's try to understand with the examples discussed b
    8 min read
    Python | Pandas Timestamp.timestamp
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Timestamp.timestamp() function returns the time expressed as the number of seco
    3 min read
    Python | Pandas Timestamp.now
    Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages that makes importing and analyzing data much easier. Pandas Timestamp.now() function returns the current time in the local timezone. It is Equiv
    3 min read
    Python | Pandas Timestamp.isoformat
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Timestamp objects represent date and time values, making them essential for wor
    2 min read
    Python | Pandas Timestamp.date
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Timestamp.date() function return a datetime object with same year, month and da
    2 min read
    Python | Pandas Timestamp.replace
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages that makes importing and analyzing data much easier. Pandas Timestamp.replace() function is used to replace the member values of the given
    3 min read
    Pandas.to_datetime()-Python
    pandas.to_datetime() converts argument(s) to datetime. This function is essential for working with date and time data, especially when parsing strings or timestamps into Python's datetime64 format used in Pandas. For Example:Pythonimport pandas as pd d = ['2025-06-21', '2025-06-22'] res = pd.to_date
    3 min read
    Python | pandas.date_range() method
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages that makes importing and analyzing data much easier. pandas.date_range() is one of the general functions in Pandas which is used to return
    4 min read
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