dataframe value_counts() Shape Error

Question

I am attempting to go through a time-series dataset and count the number of times each unique category of clothing appears for each day. Every year of my dataset works fine besides 2012. When I run my code I receive a shape broadcasting error and I cannot figure out why 2012 is causing this error but none of my other years are.

test=orders['Category']['2012']

counts = test.groupby(pd.Grouper(freq='D')).value_counts()

Here is the error the code produces

ValueError                                Traceback (most recent call last)

<ipython-input-127-bc2dbf569e47> in <module>()

      1 test=orders['Category']['2012']

----> 2 counts = test.groupby(pd.Grouper(freq='D')).value_counts()



c:\users\matthew mclaughlin\miniconda3\envs\cseclass\lib\site-packages\pandas\core\groupby.py in value_counts(self, normalize, sort, ascending, bins, dropna)

   3015 

   3016         # multi-index components

-> 3017         labels = list(map(rep, self.grouper.recons_labels)) + [lab[inc]]

   3018         levels = [ping.group_index for ping in self.grouper.groupings] + [lev]

   3019         names = self.grouper.names + [self.name]



c:\users\matthew mclaughlin\miniconda3\envs\cseclass\lib\site-packages\numpy\core\fromnumeric.py in repeat(a, repeats, axis)

    394     except AttributeError:

    395         return _wrapit(a, 'repeat', repeats, axis)

--> 396     return repeat(repeats, axis)

    397 

    398 



ValueError: operands could not be broadcast together with shape (366,) (363,)

A sample output from my data looks similar to this

Order Date

2013-01-01              Outerwear

2013-01-01            Accessories

2013-01-01       First Layer Tops

2013-01-01       First Layer Tops

2013-01-01            Accessories

2013-01-01    First Layer Bottoms

2013-01-01             Kid's Sets

2013-01-01              Outerwear

2013-01-01 Outerwear

And what the code is suppose to produce after it runs looks like this.

Order Date  Category           

2013-01-01  Outerwear              289

            First Layer Tops       230

            Accessories            190

            First Layer Bottoms    155

            Footwear                10

            Kid's Sets               3

Ultimately, I unstack this result and insert it into new columns for each category.

Recommended for you: Get network issues from WhatsUp Gold. Not end users.

Answer 1

Groupby object has no attribute called .value_counts(). If you want to value counts use apply + stack i.e

df.groupby(pd.Grouper(freq='D')).apply(lambda x : x.Category.value_counts()).stack()

Output for your test_data with additional dates.


Order Date  Category           
2013-01-01  Outerwear              3
            First Layer Tops       2
            Accessories            2
            Kid's Sets             1
            First Layer Bottoms    1
2013-01-02  Outerwear              3
            First Layer Tops       2
            Accessories            2
            Kid's Sets             1
            First Layer Bottoms    1
dtype: int64

If you trying to select categories based on the year then try boolean indexing like df[df.index.year == 2012]

dataframe value_counts() Shape Error

Sign up