CODEWITHSUNDEEP
pythonpandasdataframesum
dancemc15
dancemc15

Reputation: 628

Sum up values in a column using Pandas

I have a dataframe where one column has a list of zipcodes and the other has property values corresponding to the zipcode. I want to sum up the property values in each row according to the appropriate zipcode.

So, for example:

zip  value
2210 $5,000
2130 $3,000
2210 $2,100
2345 $1,000

I would then add up the values 

$5,000 + $2,100  = $7,100

and reap the total property value of for zipcode 2210 as $7,100.

Any help in this regard will be appreciated

Upvotes: 0

Views: 1471

Answers (1)

Harshavardhan Ramanna
Harshavardhan Ramanna

Reputation: 738

You need:

df
    zip  value
0  2210   5000
1  2130   3000
2  2210   2100
3  2345   1000

df2 = df.groupby(['zip'])['value'].sum()

df2 
zip    value
2130    3000
2210    7100
2345    1000
Name: value, dtype: int64

You can read more about it here.

Also you will need to remove the $ sign in the column values. For that you can use something along the lines of the following while reading the dataframe initially:

df = pd.read_csv('zip_value.csv', header=0,names=headers,converters={'value': lambda x: float(x.replace('$',''))})

Edit: Changed the code according to comment. To reset the index after groupby use:

df2 = df.groupby(['zip'])['value'].sum().reset_index()

Then to remove a particular column with zip value ,say, 2135 , you need

df3 = df2[df2['zip']!= 2135]

Upvotes: 1

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