CODEWITHSUNDEEP
pythonpandas
user10508414
user10508414

Reputation:

How to get the max column values count

test dataframe is below

test=pd.DataFrame({'item_name':['Steak Salad Bowl','Steak Salad Bowl','Barbacoa Salad Bowl','Meat Salad Bowl'],'item_price':[9.39,9.39,9.19,9.39]})

grps = [grp for _, grp in test.groupby('item_name', sort=False)]
print(grps[0],grps[0].shape[0])

My Expected out

item_name            count
Steak Salad Bowl     2
Meat Salad Bowl      1

Upvotes: 0

Views: 628

Answers (3)

Bharath_Raja
Bharath_Raja

Reputation: 642

Using value_counts() and head() and reset_index() and rename()

pd.DataFrame(test['item_name'].value_counts().head(2).reset_index().rename(columns = {'index': 'item_name', 'item_name' : 'count'}))

Output

             item_name  count
0     Steak Salad Bowl      2
1  Barbacoa Salad Bowl      1

Upvotes: 1

Georgina Skibinski
Georgina Skibinski

Reputation: 13387

Using nlargest(n):

test.nlargest(1, columns='item_price', keep='all')['item_name'].value_counts()

Output:

Steak Salad Bowl    2
Meat Salad Bowl     1
Name: item_name, dtype: int64

Upvotes: 1

jezrael
jezrael

Reputation: 862911

Use boolean indexing for filtering by max values, then Series.value_counts and for DataFrame DataFrame.rename_axis with DataFrame.reset_index:

df = (test.loc[test['item_price'].eq(test['item_price'].max()), 'item_name']
          .value_counts()
          .rename_axis('item_name')
          .reset_index(name='count'))
print (df)

          item_name  count
0  Steak Salad Bowl      2
1   Meat Salad Bowl      1

Or for count use GroupBy.size:

df = (test[test['item_price'].eq(test['item_price'].max())]
          .groupby('item_name')
          .size()
          .reset_index(name='count'))
print (df)
          item_name  count
0   Meat Salad Bowl      1
1  Steak Salad Bowl      2

Upvotes: 1

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