[python] Python Pandas - Find difference between two data frames

I have two data frames df1 and df2, where df2 is a subset of df1. How do I get a new data frame (df3) which is the difference between the two data frames?

In other word, a data frame that has all the rows/columns in df1 that are not in df2?

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This question is related to python pandas dataframe

The answer is


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))

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

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)

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


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)

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]

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('df1\n', df1)
print('df2\n', df2)
print('df_1notin2\n', 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

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)

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)

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 

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