Counting unique zip codes with pandas

July 23, 2023

I’m trying to count the number of each zip codes within a monthly period of time frame.

df1 = pd.DataFrame(df)
print(df[['Timestamp', 'zip']].nunique())

I get an output of:

Timestamp    8314
zip           343
dtype: int64

I don’t need the number of zip codes, I need the count of each zip code.
I was expecting:

60187 2
60542 1
60540 3

etc.

asked to post sample rows:

                              Timestamp    zip
0     Front Desk Call Log (Master Data)    NaN
1            2023-07-21 12:22:47.697000  60191
2            2023-07-21 10:55:13.311000    NaN
3            2023-07-21 10:49:06.148000  60187
4            2023-07-21 10:29:08.396000  60189
...                                 ...    ...
8309         2023-07-21 14:43:23.522000  60187
8310         2023-07-21 14:45:12.332000  60440
8311         2023-07-21 14:46:46.452000    NaN
8312         2023-07-21 17:34:11.631000  60548
8313         2023-07-21 17:39:36.358000  60133

[8314 rows x 2 columns]

>Solution :

An alternative solution to achieve the same result is by using the groupby function in combination with value_counts. Here’s the code for the alternate solution:

Group by ‘zip’ and count the occurrences of each zip code