For rows, try this, where Name is the joint index column (can be a list for multiple common columns, or specify left_on and right_on):

m = df1.merge(df2, on='Name', how='outer', suffixes=['', '_'], indicator=True)

The indicator=True setting is useful as it adds a column called _merge, with all changes between df1 and df2, categorized into 3 possible kinds: “left_only”, “right_only” or “both”.

For columns, try this:

set(df1.columns).symmetric_difference(df2.columns)

import pandas as pd
# given
df1 = pd.DataFrame({'Name':['John','Mike','Smith','Wale','Marry','Tom','Menda','Bolt','Yuswa',],
    'Age':[23,45,12,34,27,44,28,39,40]})
df2 = pd.DataFrame({'Name':['John','Smith','Wale','Tom','Menda','Yuswa',],
    'Age':[23,12,34,44,28,40]})

# find elements in df1 that are not in df2
df_1notin2 = df1[~(df1['Name'].isin(df2['Name']) & df1['Age'].isin(df2['Age']))].reset_index(drop=True)

# output:
print('df1n', df1)
print('df2n', df2)
print('df_1notin2n', df_1notin2)

# df1
#     Age   Name
# 0   23   John
# 1   45   Mike
# 2   12  Smith
# 3   34   Wale
# 4   27  Marry
# 5   44    Tom
# 6   28  Menda
# 7   39   Bolt
# 8   40  Yuswa
# df2
#     Age   Name
# 0   23   John
# 1   12  Smith
# 2   34   Wale
# 3   44    Tom
# 4   28  Menda
# 5   40  Yuswa
# df_1notin2
#     Age   Name
# 0   45   Mike
# 1   27  Marry
# 2   39   Bolt

Accepted answer Method 1 will not work for data frames with NaNs inside, as pd.np.nan != pd.np.nan. I am not sure if this is the best way, but it can be avoided by

df1[~df1.astype(str).apply(tuple, 1).isin(df2.astype(str).apply(tuple, 1))]

It’s slower, because it needs to cast data to string, but thanks to this casting pd.np.nan == pd.np.nan.

Let’s go trough the code. First we cast values to string, and apply tuple function to each row.

df1.astype(str).apply(tuple, 1)
df2.astype(str).apply(tuple, 1)

Thanks to that, we get pd.Series object with list of tuples. Each tuple contains whole row from df1/df2.
Then we apply isin method on df1 to check if each tuple "is in" df2.
The result is pd.Series with bool values. True if tuple from df1 is in df2. In the end, we negate results with ~ sign, and applying filter on df1. Long story short, we get only those rows from df1 that are not in df2.

To make it more readable, we may write it as:

df1_str_tuples = df1.astype(str).apply(tuple, 1)
df2_str_tuples = df2.astype(str).apply(tuple, 1)
df1_values_in_df2_filter = df1_str_tuples.isin(df2_str_tuples)
df1_values_not_in_df2 = df1[~df1_values_in_df2_filter]

edit2, I figured out a new solution without the need of setting index

newdf=pd.concat([df1,df2]).drop_duplicates(keep=False)

Okay i found the answer of highest vote already contain what I have figured out. Yes, we can only use this code on condition that there are no duplicates in each two dfs.

I have a tricky method. First we set ’Name’ as the index of two dataframe given by the question. Since we have same ’Name’ in two dfs, we can just drop the ’smaller’ df’s index from the ‘bigger’ df.
Here is the code.

df1.set_index('Name',inplace=True)
df2.set_index('Name',inplace=True)
newdf=df1.drop(df2.index)

A slight variation of the nice @liangli’s solution that does not require to change the index of existing dataframes:

newdf = df1.drop(df1.join(df2.set_index('Name').index))

Perhaps a simpler one-liner, with identical or different column names. Worked even when df2[‘Name2’] contained duplicate values.

newDf = df1.set_index('Name1')
           .drop(df2['Name2'], errors='ignore')
           .reset_index(drop=False)

Finding difference by index. Assuming df1 is a subset of df2 and the indexes are carried forward when subsetting

df1.loc[set(df1.index).symmetric_difference(set(df2.index))].dropna()

# Example

df1 = pd.DataFrame({"gender":np.random.choice(['m','f'],size=5), "subject":np.random.choice(["bio","phy","chem"],size=5)}, index = [1,2,3,4,5])

df2 =  df1.loc[[1,3,5]]

df1

 gender subject
1      f     bio
2      m    chem
3      f     phy
4      m     bio
5      f     bio

df2

  gender subject
1      f     bio
3      f     phy
5      f     bio

df3 = df1.loc[set(df1.index).symmetric_difference(set(df2.index))].dropna()

df3

  gender subject
2      m    chem
4      m     bio

In addition to accepted answer, I would like to propose one more wider solution that can find a 2D set difference of two dataframes with any index/columns (they might not coincide for both datarames). Also method allows to setup tolerance for float elements for dataframe comparison (it uses np.isclose)

import numpy as np
import pandas as pd

def get_dataframe_setdiff2d(df_new: pd.DataFrame, 
                            df_old: pd.DataFrame, 
                            rtol=1e-03, atol=1e-05) -> pd.DataFrame:
    """Returns set difference of two pandas DataFrames"""

    union_index = np.union1d(df_new.index, df_old.index)
    union_columns = np.union1d(df_new.columns, df_old.columns)

    new = df_new.reindex(index=union_index, columns=union_columns)
    old = df_old.reindex(index=union_index, columns=union_columns)

    mask_diff = ~np.isclose(new, old, rtol, atol)

    df_bool = pd.DataFrame(mask_diff, union_index, union_columns)

    df_diff = pd.concat([new[df_bool].stack(),
                         old[df_bool].stack()], axis=1)

    df_diff.columns = ["New", "Old"]

    return df_diff

Example:

In [1]

df1 = pd.DataFrame({'A':[2,1,2],'C':[2,1,2]})
df2 = pd.DataFrame({'A':[1,1],'B':[1,1]})

print("df1:n", df1, "n")

print("df2:n", df2, "n")

diff = get_dataframe_setdiff2d(df1, df2)

print("diff:n", diff, "n")

Out [1]

df1:
   A  C
0  2  2
1  1  1
2  2  2 

df2:
   A  B
0  1  1
1  1  1 

diff:
     New  Old
0 A  2.0  1.0
  B  NaN  1.0
  C  2.0  NaN
1 B  NaN  1.0
  C  1.0  NaN
2 A  2.0  NaN
  C  2.0  NaN 

As mentioned here
that

df1[~df1.apply(tuple,1).isin(df2.apply(tuple,1))]

is correct solution but it will produce wrong output if

df1=pd.DataFrame({'A':[1],'B':[2]})
df2=pd.DataFrame({'A':[1,2,3,3],'B':[2,3,4,4]})

In that case above solution will give
Empty DataFrame, instead you should use concat method after removing duplicates from each datframe.

Use concate with drop_duplicates

df1=df1.drop_duplicates(keep="first") 
df2=df2.drop_duplicates(keep="first") 
pd.concat([df1,df2]).drop_duplicates(keep=False)

Using the lambda function you can filter the rows with _merge value “left_only” to get all the rows in df1 which are missing from df2

df3 = df1.merge(df2, how = 'outer' ,indicator=True).loc[lambda x :x['_merge']=='left_only']
df

I had issues with handling duplicates when there were duplicates on one side and at least one on the other side, so I used Counter.collections to do a better diff, ensuring both sides have the same count. This doesn’t return duplicates, but it won’t return any if both sides have the same count.

from collections import Counter

def diff(df1, df2, on=None):
    """
    :param on: same as pandas.df.merge(on) (a list of columns)
    """
    on = on if on else df1.columns
    df1on = df1[on]
    df2on = df2[on]
    c1 = Counter(df1on.apply(tuple, 'columns'))
    c2 = Counter(df2on.apply(tuple, 'columns'))
    c1c2 = c1-c2
    c2c1 = c2-c1
    df1ondf2on = pd.DataFrame(list(c1c2.elements()), columns=on)
    df2ondf1on = pd.DataFrame(list(c2c1.elements()), columns=on)
    df1df2 = df1.merge(df1ondf2on).drop_duplicates(subset=on)
    df2df1 = df2.merge(df2ondf1on).drop_duplicates(subset=on)
    return pd.concat([df1df2, df2df1])

> df1 = pd.DataFrame({'a': [1, 1, 3, 4, 4]})
> df2 = pd.DataFrame({'a': [1, 2, 3, 4, 4]})
> diff(df1, df2)
   a
0  1
0  2

Defining our dataframes:

df1 = pd.DataFrame({
    'Name':
        ['John','Mike','Smith','Wale','Marry','Tom','Menda','Bolt','Yuswa'],
    'Age':
        [23,45,12,34,27,44,28,39,40]
})

df2 = df1[df1.Name.isin(['John','Smith','Wale','Tom','Menda','Yuswa'])

df1

    Name  Age
0   John   23
1   Mike   45
2  Smith   12
3   Wale   34
4  Marry   27
5    Tom   44
6  Menda   28
7   Bolt   39
8  Yuswa   40

df2

    Name  Age
0   John   23
2  Smith   12
3   Wale   34
5    Tom   44
6  Menda   28
8  Yuswa   40

The difference between the two would be:

df1[~df1.isin(df2)].dropna()

    Name   Age
1   Mike  45.0
4  Marry  27.0
7   Bolt  39.0

Where:

• df1.isin(df2) returns the rows in df1 that are also in df2.
• ~ (Element-wise logical NOT) in front of the expression negates the results, so we get the elements in df1 that are NOT in df2–the difference between the two.
• .dropna() drops the rows with NaN presenting the desired output

Note This only works if len(df1) >= len(df2). If df2 is longer than df1 you can reverse the expression: df2[~df2.isin(df1)].dropna()

I found the deepdiff library is a wonderful tool that also extends well to dataframes if different detail is required or ordering matters. You can experiment with diffing to_dict('records'), to_numpy(), and other exports:

import pandas as pd
from deepdiff import DeepDiff

df1 = pd.DataFrame({
    'Name':
        ['John','Mike','Smith','Wale','Marry','Tom','Menda','Bolt','Yuswa'],
    'Age':
        [23,45,12,34,27,44,28,39,40]
})

df2 = df1[df1.Name.isin(['John','Smith','Wale','Tom','Menda','Yuswa'])]

DeepDiff(df1.to_dict(), df2.to_dict())
# {'dictionary_item_removed': [root['Name'][1], root['Name'][4], root['Name'][7], root['Age'][1], root['Age'][4], root['Age'][7]]}

Pandas now offers a new API to do data frame diff: pandas.DataFrame.compare

df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

There is a new method in pandas DataFrame.compare that compare 2 different dataframes and return which values changed in each column for the data records.

Example

First Dataframe

Id Customer Status      Date
1      ABC   Good  Mar 2023
2      BAC   Good  Feb 2024
3      CBA    Bad  Apr 2022

Second Dataframe

Id Customer Status      Date
1      ABC    Bad  Mar 2023
2      BAC   Good  Feb 2024
5      CBA   Good  Apr 2024

Comparing Dataframes

print("Dataframe difference -- n")
print(df1.compare(df2))

print("Dataframe difference keeping equal values -- n")
print(df1.compare(df2, keep_equal=True))

print("Dataframe difference keeping same shape -- n")
print(df1.compare(df2, keep_shape=True))

print("Dataframe difference keeping same shape and equal values -- n")
print(df1.compare(df2, keep_shape=True, keep_equal=True))

Result

Dataframe difference -- 

    Id       Status            Date          
  self other   self other      self     other
0  NaN   NaN   Good   Bad       NaN       NaN
2  3.0   5.0    Bad  Good  Apr 2022  Apr 2024

Dataframe difference keeping equal values -- 

    Id       Status            Date          
  self other   self other      self     other
0    1     1   Good   Bad  Mar 2023  Mar 2023
2    3     5    Bad  Good  Apr 2022  Apr 2024

Dataframe difference keeping same shape -- 

    Id       Customer       Status            Date          
  self other     self other   self other      self     other
0  NaN   NaN      NaN   NaN   Good   Bad       NaN       NaN
1  NaN   NaN      NaN   NaN    NaN   NaN       NaN       NaN
2  3.0   5.0      NaN   NaN    Bad  Good  Apr 2022  Apr 2024

Dataframe difference keeping same shape and equal values -- 

    Id       Customer       Status            Date          
  self other     self other   self other      self     other
0    1     1      ABC   ABC   Good   Bad  Mar 2023  Mar 2023
1    2     2      BAC   BAC   Good  Good  Feb 2024  Feb 2024
2    3     5      CBA   CBA    Bad  Good  Apr 2022  Apr 2024

Symmetric Difference

If you are interested in the rows that are only in one of the dataframes but not both, you are looking for the set difference:

pd.concat([df1,df2]).drop_duplicates(keep=False)

⚠️ Only works, if both dataframes do not contain any duplicates.

Set Difference / Relational Algebra Difference

If you are interested in the relational algebra difference / set difference, i.e. df1-df2 or df1df2:

pd.concat([df1,df2,df2]).drop_duplicates(keep=False) 

⚠️ Only works, if both dataframes do not contain any duplicates.

Try this one:
df_new = df1.merge(df2, how='outer', indicator=True).query('_merge == "left_only"').drop('_merge', 1)

It will result a new dataframe with the differences: the values that exist in df1 but not in df2.

Another possible solution is to use numpy broadcasting:

df1[np.all(~np.all(df1.values == df2.values[:, None], axis=2), axis=0)]

Output:

    Name  Age
1   Mike   45
4  Marry   27
7   Bolt   39