CODEWITHSUNDEEP
pythonpandascsv
vishal
vishal

Reputation: 95

How to compare two CSV files and get the difference?

I have two CSV files,

a1.csv

city,state,link
Aguila,Arizona,https://www.glendaleaz.com/planning/documents/AppendixAZONING.pdf
AkChin,Arizona,http://www.maricopa-az.gov/zoningcode/wp-content/uploads/2014/05/Zoning-Code-Rewrite-Public-Review-Draft-3-Tracked-Edits-lowres1.pdf
Aguila,Arizona,http://www.co.apache.az.us/planning-and-zoning-division/zoning-ordinances/

a2.csv

city,state,link
Aguila,Arizona,http://www.co.apache.az.us

I want to get the difference.

Here is my attempt:

import pandas as pd

a = pd.read_csv('a1.csv')
b = pd.read_csv('a2.csv')

mask = a.isin(b.to_dict(orient='list'))
# Reverse the mask and remove null rows.
# Upside is that index of original rows that
# are now gone are preserved (see result).
c = a[~mask].dropna()
print c

Expected Output:

city,state,link
Aguila,Arizona,https://www.glendaleaz.com/planning/documents/AppendixAZONING.pdf
AkChin,Arizona,http://www.maricopa-az.gov/zoningcode/wp-content/uploads/2014/05/Zoning-Code-Rewrite-Public-Review-Draft-3-Tracked-Edits-lowres1.pdf

But I am getting an error:

Empty DataFrame
Columns: [city, state, link]
Index: []**

I want to check based on the first two rows, then if they are the same, remove it off.

Upvotes: 3

Views: 11206

Answers (2)

shindig_
shindig_

Reputation: 38

First, concatenate the DataFrames, then drop the duplicates while still keeping the first one. Then reset the index to keep it consistent.

import pandas as pd

a = pd.read_csv('a1.csv')
b = pd.read_csv('a2.csv')
c = pd.concat([a,b], axis=0)

c.drop_duplicates(keep='first', inplace=True) # Set keep to False if you don't want any
                                              # of the duplicates at all
c.reset_index(drop=True, inplace=True)
print(c)

Upvotes: 2

Yilun Zhang
Yilun Zhang

Reputation: 9008

You can use pandas to read in two files, join them and remove all duplicate rows:

import pandas as pd
a = pd.read_csv('a1.csv')
b = pd.read_csv('a2.csv')
ab = pd.concat([a,b], axis=0)
ab.drop_duplicates(keep=False)

Reference: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html

Upvotes: 5

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