CODEWITHSUNDEEP
pythonsortingpandascountgroup-by
Rubans
Rubans

Reputation: 4478

Count and Sort with Pandas

I have a dataframe for values form a file by which I have grouped by two columns, which return a count of the aggregation. Now I want to sort by the max count value, however I get the following error:

KeyError: 'count'

Looks the group by agg count column is some sort of index so not sure how to do this, I'm a beginner to Python and Panda. Here's the actual code, please let me know if you need more detail:

def answer_five():
    df = census_df#.set_index(['STNAME'])
    df = df[df['SUMLEV'] == 50]
    df = df[['STNAME','CTYNAME']].groupby(['STNAME']).agg(['count']).sort(['count'])
    #df.set_index(['count'])
    print(df.index)
    # get sorted count max item
    return df.head(5)

Upvotes: 51

Views: 183360

Answers (5)

Filipe Gomes
Filipe Gomes

Reputation: 328

For sort the rows by count of a colum, you can do this:

sorted_index = df['col'].value_counts().index
df.set_index('col').loc[sorted_index].reset_index()

If you want to keep the old index, do this:

sorted_index = df['col'].value_counts().index
df['index'] = df.index
df.set_index('col', drop=True).loc[sorted_index].reset_index().set_index('index', drop=True)

Upvotes: 0

Ahmad
Ahmad

Reputation: 9658

Some of the existing answers are outdated. The following solution works for listing a column and the frequency of its distinct values:

df = df[col].value_counts(ascending=False).reset_index()

Upvotes: 7

Angelin Nadar
Angelin Nadar

Reputation: 9300

I agree with @Christoph Schranz to take slice a series from dataframe

df[['STNAME','CTYNAME']].groupby('STNAME')['CTYNAME'].count().nlargest(3)

Upvotes: 2

jezrael
jezrael

Reputation: 862481

I think you need add reset_index, then parameter ascending=False to sort_values because sort return:

FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....) .sort_values(['count'], ascending=False)

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] \
                             .count() \
                             .reset_index(name='count') \
                             .sort_values(['count'], ascending=False) \
                             .head(5)

Sample:

df = pd.DataFrame({'STNAME':list('abscscbcdbcsscae'),
                   'CTYNAME':[4,5,6,5,6,2,3,4,5,6,4,5,4,3,6,5]})

print (df)
    CTYNAME STNAME
0         4      a
1         5      b
2         6      s
3         5      c
4         6      s
5         2      c
6         3      b
7         4      c
8         5      d
9         6      b
10        4      c
11        5      s
12        4      s
13        3      c
14        6      a
15        5      e

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] \
                             .count() \
                             .reset_index(name='count') \
                             .sort_values(['count'], ascending=False) \
                             .head(5)

print (df)
  STNAME  count
2      c      5
5      s      4
1      b      3
0      a      2
3      d      1

But it seems you need Series.nlargest:

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].count().nlargest(5)

or:

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].size().nlargest(5)

The difference between size and count is:

size counts NaN values, count does not.

Sample:

df = pd.DataFrame({'STNAME':list('abscscbcdbcsscae'),
                   'CTYNAME':[4,5,6,5,6,2,3,4,5,6,4,5,4,3,6,5]})

print (df)
    CTYNAME STNAME
0         4      a
1         5      b
2         6      s
3         5      c
4         6      s
5         2      c
6         3      b
7         4      c
8         5      d
9         6      b
10        4      c
11        5      s
12        4      s
13        3      c
14        6      a
15        5      e

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME']
                             .size()
                             .nlargest(5)
                             .reset_index(name='top5')
print (df)
  STNAME  top5
0      c     5
1      s     4
2      b     3
3      a     2
4      d     1

Upvotes: 104

Christoph Schranz
Christoph Schranz

Reputation: 915

I don't know exactly how your df looks like. But if you have to sort the frequency of several categories by its count, it is easier to slice a Series from the df and sort the series:

series = df.count().sort_values(ascending=False)
series.head()

Note that this series will use the name of the category as index!

Upvotes: 21

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