Comparing two dataframes and getting the differences

I got this solution. Does this help you ?

text = """df1:
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange 8.6 Orange
2013-11-24 Apple 7.6 Green
2013-11-24 Celery 10.2 Green


df2:
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange 8.6 Orange
2013-11-24 Apple 7.6 Green
2013-11-24 Celery 10.2 Green
2013-11-25 Apple 22.1 Red
2013-11-25 Orange 8.6 Orange






argetz45
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange 118.6 Orange
2013-11-24 Apple 74.6 Green
2013-11-24 Celery 10.2 Green
2013-11-25     Nuts    45.8 Brown
2013-11-25 Apple 22.1 Red
2013-11-25 Orange 8.6 Orange
2013-11-26   Pear 102.54    Pale"""

.

from collections import OrderedDict
import re


r = re.compile('([a-zA-Z\d]+).*\n'
'(20\d\d-[01]\d-[0123]\d.+\n?'
'(.+\n?)*)'
'(?=[ \n]*\Z'
'|'
'\n+[a-zA-Z\d]+.*\n'
'20\d\d-[01]\d-[0123]\d)')


r2 = re.compile('((20\d\d-[01]\d-[0123]\d) +([^\d.]+)(?<! )[^\n]+)')


d = OrderedDict()
bef = []


for m in r.finditer(text):
li = []
for x in r2.findall(m.group(2)):
if not any(x[1:3]==elbef for elbef in bef):
bef.append(x[1:3])
li.append(x[0])
d[m.group(1)] = li




for name,lu in d.iteritems():
print '%s\n%s\n' % (name,'\n'.join(lu))

result

df1
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange 8.6 Orange
2013-11-24 Apple 7.6 Green
2013-11-24 Celery 10.2 Green


df2
2013-11-25 Apple 22.1 Red
2013-11-25 Orange 8.6 Orange


argetz45
2013-11-25     Nuts    45.8 Brown
2013-11-26   Pear 102.54    Pale

This approach, df1 != df2, works only for dataframes with identical rows and columns. In fact, all dataframes axes are compared with _indexed_same method, and exception is raised if differences found, even in columns/indices order.

If I got you right, you want not to find changes, but symmetric difference. For that, one approach might be concatenate dataframes:

>>> df = pd.concat([df1, df2])
>>> df = df.reset_index(drop=True)

group by

>>> df_gpby = df.groupby(list(df.columns))

get index of unique records

>>> idx = [x[0] for x in df_gpby.groups.values() if len(x) == 1]

filter

>>> df.reindex(idx)
Date   Fruit   Num   Color
9  2013-11-25  Orange   8.6  Orange
8  2013-11-25   Apple  22.1     Red

Building on alko's answer that almost worked for me, except for the filtering step (where I get: ValueError: cannot reindex from a duplicate axis), here is the final solution I used:

# join the dataframes
united_data = pd.concat([data1, data2, data3, ...])
# group the data by the whole row to find duplicates
united_data_grouped = united_data.groupby(list(united_data.columns))
# detect the row indices of unique rows
uniq_data_idx = [x[0] for x in united_data_grouped.indices.values() if len(x) == 1]
# extract those unique values
uniq_data = united_data.iloc[uniq_data_idx]

Passing the dataframes to concat in a dictionary, results in a multi-index dataframe from which you can easily delete the duplicates, which results in a multi-index dataframe with the differences between the dataframes:

import sys
if sys.version_info[0] < 3:
from StringIO import StringIO
else:
from io import StringIO
import pandas as pd


DF1 = StringIO("""Date       Fruit  Num  Color
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange  8.6 Orange
2013-11-24 Apple   7.6 Green
2013-11-24 Celery 10.2 Green
""")
DF2 = StringIO("""Date       Fruit  Num  Color
2013-11-24 Banana 22.1 Yellow
2013-11-24 Orange  8.6 Orange
2013-11-24 Apple   7.6 Green
2013-11-24 Celery 10.2 Green
2013-11-25 Apple  22.1 Red
2013-11-25 Orange  8.6 Orange""")




df1 = pd.read_table(DF1, sep='\s+')
df2 = pd.read_table(DF2, sep='\s+')
#%%
dfs_dictionary = {'DF1':df1,'DF2':df2}
df=pd.concat(dfs_dictionary)
df.drop_duplicates(keep=False)

Result:

             Date   Fruit   Num   Color
DF2 4  2013-11-25   Apple  22.1     Red
5  2013-11-25  Orange   8.6  Orange

There is a simpler solution that is faster and better, and if the numbers are different can even give you quantities differences:

df1_i = df1.set_index(['Date','Fruit','Color'])
df2_i = df2.set_index(['Date','Fruit','Color'])
df_diff = df1_i.join(df2_i,how='outer',rsuffix='_').fillna(0)
df_diff = (df_diff['Num'] - df_diff['Num_'])

Here df_diff is a synopsis of the differences. You can even use it to find the differences in quantities. In your example:

Explanation: Similarly to comparing two lists, to do it efficiently we should first order them then compare them (converting the list to sets/hashing would also be fast; both are an incredible improvement to the simple O(N^2) double comparison loop

Note: the following code produces the tables:

df1=pd.DataFrame({
'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24'],
'Fruit':['Banana','Orange','Apple','Celery'],
'Num':[22.1,8.6,7.6,10.2],
'Color':['Yellow','Orange','Green','Green'],
})
df2=pd.DataFrame({
'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24','2013-11-25','2013-11-25'],
'Fruit':['Banana','Orange','Apple','Celery','Apple','Orange'],
'Num':[22.1,8.6,7.6,10.2,22.1,8.6],
'Color':['Yellow','Orange','Green','Green','Red','Orange'],
})

One important detail to notice is that your data has duplicate index values, so to perform any straightforward comparison we need to turn everything as unique with df.reset_index() and therefore we can perform selections based on conditions. Once in your case the index is defined, I assume that you would like to keep de index so there are a one-line solution:

[~df2.reset_index().isin(df1.reset_index())].dropna().set_index('Date')

Once the objective from a pythonic perspective is to improve readability, we can break a little bit:

# keep the index name, if it does not have a name it uses the default name
index_name = df.index.name if df.index.name else 'index'


# setting the index to become unique
df1 = df1.reset_index()
df2 = df2.reset_index()


# getting the differences to a Dataframe
df_diff = df2[~df2.isin(df1)].dropna().set_index(index_name)

# given
df1=pd.DataFrame({'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24'],
'Fruit':['Banana','Orange','Apple','Celery'],
'Num':[22.1,8.6,7.6,10.2],
'Color':['Yellow','Orange','Green','Green']})
df2=pd.DataFrame({'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24','2013-11-25','2013-11-25'],
'Fruit':['Banana','Orange','Apple','Celery','Apple','Orange'],
'Num':[22.1,8.6,7.6,1000,22.1,8.6],
'Color':['Yellow','Orange','Green','Green','Red','Orange']})


# find which rows are in df2 that aren't in df1 by Date and Fruit
df_2notin1 = df2[~(df2['Date'].isin(df1['Date']) & df2['Fruit'].isin(df1['Fruit']) )].dropna().reset_index(drop=True)


# output
print('df_2notin1\n', df_2notin1)
#      Color        Date   Fruit   Num
# 0     Red  2013-11-25   Apple  22.1
# 1  Orange  2013-11-25  Orange   8.6

Founder a simple solution here:

https://stackoverflow.com/a/47132808/9656339

pd.concat([df1, df2]).loc[df1.index.symmetric_difference(df2.index)]

Hope this would be useful to you. ^o^

df1 = pd.DataFrame({'date': ['0207', '0207'], 'col1': [1, 2]})
df2 = pd.DataFrame({'date': ['0207', '0207', '0208', '0208'], 'col1': [1, 2, 3, 4]})
print(f"df1(Before):\n{df1}\ndf2:\n{df2}")
"""
df1(Before):
date  col1
0  0207     1
1  0207     2


df2:
date  col1
0  0207     1
1  0207     2
2  0208     3
3  0208     4
"""


old_set = set(df1.index.values)
new_set = set(df2.index.values)
new_data_index = new_set - old_set
new_data_list = []
for idx in new_data_index:
new_data_list.append(df2.loc[idx])


if len(new_data_list) > 0:
df1 = df1.append(new_data_list)
print(f"df1(After):\n{df1}")
"""
df1(After):
date  col1
0  0207     1
1  0207     2
2  0208     3
3  0208     4
"""

I tried this method, and it worked. I hope it can help too:

"""Identify differences between two pandas DataFrames"""
df1.sort_index(inplace=True)
df2.sort_index(inplace=True)
df_all = pd.concat([df1, df12], axis='columns', keys=['First', 'Second'])
df_final = df_all.swaplevel(axis='columns')[df1.columns[1:]]
df_final[df_final['change this to one of the columns'] != df_final['change this to one of the columns']]

Updating and placing, somewhere it will be easier for others to find, ling's comment upon jur's response above.

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

Testing with these DataFrames:

# with import pandas as pd


df1 = pd.DataFrame({
'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24'],
'Fruit':['Banana','Orange','Apple','Celery'],
'Num':[22.1,8.6,7.6,10.2],
'Color':['Yellow','Orange','Green','Green'],
})


df2 = pd.DataFrame({
'Date':['2013-11-24','2013-11-24','2013-11-24','2013-11-24','2013-11-25','2013-11-25'],
'Fruit':['Banana','Orange','Apple','Celery','Apple','Orange'],
'Num':[22.1,8.6,7.6,10.2,22.1,8.6],
'Color':['Yellow','Orange','Green','Green','Red','Orange'],
})

Results in this:

# for df1


Date   Fruit   Num   Color
0  2013-11-24  Banana  22.1  Yellow
1  2013-11-24  Orange   8.6  Orange
2  2013-11-24   Apple   7.6   Green
3  2013-11-24  Celery  10.2   Green




# for df2


Date   Fruit   Num   Color
0  2013-11-24  Banana  22.1  Yellow
1  2013-11-24  Orange   8.6  Orange
2  2013-11-24   Apple   7.6   Green
3  2013-11-24  Celery  10.2   Green
4  2013-11-25   Apple  22.1     Red
5  2013-11-25  Orange   8.6  Orange




# for df_diff


Date   Fruit   Num   Color
4  2013-11-25   Apple  22.1     Red
5  2013-11-25  Orange   8.6  Orange

# THIS WORK FOR ME


# Get all diferent values
df3 = pd.merge(df1, df2, how='outer', indicator='Exist')
df3 = df3.loc[df3['Exist'] != 'both']




# If you like to filter by a common ID
df3  = pd.merge(df1, df2, on="Fruit", how='outer', indicator='Exist')
df3  = df3.loc[df3['Exist'] != 'both']

Since pandas >= 1.1.0 we have DataFrame.compare and Series.compare.

Note: the method can only compare identically-labeled DataFrame objects, this means DataFrames with identical row and column labels.

df1 = pd.DataFrame({'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, np.NaN, 9]})


df2 = pd.DataFrame({'A': [1, 99, 3],
'B': [4, 5, 81],
'C': [7, 8, 9]})


A  B    C
0  1  4  7.0
1  2  5  NaN
2  3  6  9.0


A   B  C
0   1   4  7
1  99   5  8
2   3  81  9

df1.compare(df2)


A          B          C
self other self other self other
1  2.0  99.0  NaN   NaN  NaN   8.0
2  NaN   NaN  6.0  81.0  NaN   NaN

Get the existing data from df2 into df1:

dfe = df2[df2["Fruit"].isin(df1["Fruit"])]

Get the non-existing data from df2 into df1:

dfn = df2[~ df2["Fruit"].isin(df1["Fruit"])]

You can use more than one comparison.

You can find the difference between DataFrame row counts:

df2.value_counts().sub(df1.value_counts(), fill_value=0)

Output:

Date        Fruit   Num     Color
2013-11-24  Apple   7.6     Green     0.0
Banana  22.1    Yellow    0.0
Celery  10.2    Green    -1.0
1000.0  Green     1.0
Orange  8.6     Orange    0.0
2013-11-25  Apple   22.1    Red       1.0
Orange  8.6     Orange    1.0
dtype: float6

use merge outer to find the left outer values whose value is null

txt1="""Date,Fruit,Num,Color
2013-11-24,Banana,22.1,Yellow
2013-11-24,Orange,8.6,Orange
2013-11-24,Apple,7.6,Green
2013-11-24,Celery,10.2,Green"""


txt2="""Date,Fruit,Num,Color
2013-11-24,Banana,22.1,Yellow
2013-11-24,Orange,8.6,Orange
2013-11-24,Apple,7.6,Green
2013-11-24,Celery,10.2,Green
2013-11-25,Apple,22.1,Red
2013-11-25,Orange,8.6,Orange"""


from io import StringIO
f = StringIO(txt1)
df1 = pd.read_table(f,sep =',')
df1.set_index('Date',inplace=True)


f = StringIO(txt2)
df2 = pd.read_table(f,sep =',')
df2.set_index('Date',inplace=True)


df3 =pd.merge(df2, df1, left_index=True, right_index=True,  how='outer',
indicator=True
,suffixes=("", "_left")
).query("_merge=='left_only'")
remove_columns=[item for item in df3.columns if '_left' in item]
remove_columns.append('_merge')
df3=df3.drop(columns=remove_columns)
print(df3)

output:

         Date   Fruit   Num  Color
0  2013-11-25   Apple  22.1     Red
1  2013-11-25  Orange   8.6  Orange