Merge two dataframes pandas with same column names

How To Merge Two Pandas Dataframes On A Column With Code Examples

Hello everyone, in this post we will look at how to solve How To Merge Two Pandas Dataframes On A Column in programming.

df_outer = pd.merge(df1, df2, on='id', how='outer') #here id is common column
df_outer

You'll see some examples of different ways to solve the How To Merge Two Pandas Dataframes On A Column problem further down in this article.

df_3 = pd.concat([df_1, df_2])

import pandas as pd 
import numpy as np
df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'] * 3,
                    'weight': ['high', 'medium', 'low'] * 3,
                    'price': np.random.randint(0, 15, 9)})
df2 = pd.DataFrame({'pazham': ['apple', 'orange', 'pine'] * 2,
                    'kilo': ['high', 'low'] * 3,
                    'price': np.random.randint(0, 15, 6)})
out = df1.merge(df2,left_on=('fruit','weight'),right_on=('pazham','kilo'),how='inner',suffixes=('_left','_right')).head(10)

import pandas as pd
T1 = pd.merge(T1, T2, on=T1.index, how='outer')

#suppose you have two dataframes df1 and df2, and 
#you need to merge them along the column id
df_merge_col = pd.merge(df1, df2, on='id')

df["period"] = df["Year"] + df["quarter"]

We were able to solve the How To Merge Two Pandas Dataframes On A Column issue by looking at a number of other examples.

Can you merge more than 2 DataFrames in pandas?

Just simply merge with DATE as the index and merge using OUTER method (to get all the data). Now, basically load all the files you have as data frame into a list. And, then merge the files using merge or reduce function. Note: you can add as many data-frames inside the above list.02-Jun-2017

How do I merge two tables in pandas?

Merge, join, concatenate and compare

• pd. concat( objs, axis=0, join="outer", ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True, )
• >>> In [6]: result = pd. concat(frames, keys=["x", "y", "z"])
• >>> In [7]: result.
• frames = [ process_your_file(f) for f in files ] result = pd.

How do I merge 5 Dataframes in Python?

Pandas merge() function is used to merge multiple Dataframes. We can use either pandas. merge() or DataFrame. merge() to merge multiple Dataframes.16-Jan-2022

How do I combine multiple data frames into one?

The concat() function in pandas is used to append either columns or rows from one DataFrame to another. The concat() function does all the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.02-Dec-2020

How do I merge two Dataframes with the same column names?

Approach

• Import module.
• Create or load first dataframe.
• Create or load second dataframe.
• Concatenate on the basis of same column names.
• Display result.

How do I merge two Dataframes in pandas on index?

How to Merge Two Pandas DataFrames on Index

• Use join: By default, this performs a left join. df1. join(df2)
• Use merge. By default, this performs an inner join. pd. merge(df1, df2, left_index=True, right_index=True)
• Use concat. By default, this performs an outer join.

What is the difference between merge and join in pandas?

Both join and merge can be used to combines two dataframes but the join method combines two dataframes on the basis of their indexes whereas the merge method is more versatile and allows us to specify columns beside the index to join on for both dataframes.28-Mar-2022

How do I concatenate two Dataframes vertically?

To concatenate DataFrames vertically in Pandas, use the concat(~) method.

How do I merge a list of Dataframes in python?

concat() to merge a list of DataFrames into a single DataFrame. Call pandas. concat(df_list) with df_list as a list of pandas. DataFrame s with the same column labels to merge the DataFrame s into a single DataFrame .

How do I merge two Dataframes in python with different column names?

Different column names are specified for merges in Pandas using the “left_on” and “right_on” parameters, instead of using only the “on” parameter. Merging dataframes with different names for the joining variable is achieved using the left_on and right_on arguments to the pandas merge function.

Pandas join() function

This function allows the lowest level of control. It will join the rows from the two tables based on a common column or index. Have a look at the illustration below to understand various type of joins.

Type of join operations

Now, let’s have a look at the coding part.

df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
df2 = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
                      'B': ['B0', 'B1', 'B2']})
print(df1)
print(df2)

Defining two sample DataFrames

The output of the above code snippet is:

  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Now, let’s join the two DataFrames.

df1.join(df2, lsuffix='_caller', rsuffix='_other')

The output of the above join operation will be:

  key_caller   A key_other    B
0         K0  A0        K0   B0
1         K1  A1        K1   B1
2         K2  A2        K2   B2
3         K3  A3       NaN  NaN
4         K4  A4       NaN  NaN
5         K5  A5       NaN  NaN

Explanation :

• By default, join() does a left join, but you can change the type of join by providing a value for the how parameter in the join() function as how='type_of_join'
• The parameterlsuffix is the suffix that will be added to the column name from the left frame’s overlapping columns.
• The parameter rsuffix is the suffix that will be added to the column name from the right frame’s overlapping columns.

Pandas merge() function

This function is also used to combine or join two DataFrames with the same columns or indices. More or less, it does the same thing as join().

However, merge() allows us to specify what columns to join on for both the left and right DataFrames.

merge() is useful when we don’t want to join on the index.

df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [5, 6, 7, 8]})
print(df1)
print(df2)

Defining two sample DataFrames

The output of the above code is:

    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Now, let’s merge the two DataFrames.

df1.merge(df2, left_on='lkey', right_on='rkey')

The output of the above code is:

  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

Explanation:

• The parameter left_on is the column or index level names to join on in the left DataFrame.
• The parameter right_on is the column or index level names to join on in the right DataFrame.
• By default, the merge() function performs an inner join, but you can change it by passing the parameter value how='type_of_join'.

Pandas concat() function

This function is used to append one (or more) DataFrames stacked below the other (or sideways, depending on whether the axis option is set to 0 or 1).

Also, make sure that the dimensions of the DataFrames should match along the axis while concatenating.

df1 = pd.DataFrame({'Key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 
                    'data1': range(7)})
df2 = pd.DataFrame({'Key': ['a', 'b', 'd'], 
                    'data2': range(3)})
print(df1)
print(df2)

The output of the above code is:

   Key  data1
0   b   0
1   b   1
2   a   2
3   c   3
4   a   4
5   a   5
6   b   6
    Key data2
0   a   0
1   b   1
2   d   2

Now, let’s concatenate the DataFrames.

The output of the above code is:

   Key data1 data2
0   b   0     NaN
1   b   1     NaN
2   a   2     NaN
3   c   3     NaN
4   a   4     NaN
5   a   5     NaN
6   b   6     NaN
0   a   NaN   0
1   b   NaN   1
2   d   NaN   2

Explanation:

• The dataframe df2 is appended after df1.
• NaN values denote that the values for that column are not present in the DataFrame.

Which to use and when to use?

1. The join() method works best when we are joining DataFrames on their indexes.
2. The merge() method is more versatile and allows us to specify columns, besides the index to join on, for both DataFrames.
3. We cannot use concat() if our DataFrames’ dimensions do not match along the axis in which we are trying to concatenate.
4. The concat() has inner (default) and outer joins only, whereas merge() has left, right, outer, and inner (default) joins.

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