Pandas Groupby: Count and mean combined

问题

Working with PANDAS to try and summarise a dataframe as a count of certain categories, as well as the means sentiment score for these categories.

There is table full of strings which have different sentiment scores, and I want to group each text source by saying how many posts they have, as well as the average sentiment of these posts.

My (simplified) dataframe looks like this:

source    text              sent
--------------------------------
bar       some string       0.13
foo       alt string        -0.8
bar       another str       0.7
foo       some text         -0.2
foo       more text         -0.5

The output from this should be something like this:

source    count     mean_sent
-----------------------------
foo       3         -0.5
bar       2         0.415

The answer is somewhere along the lines of:

df['sent'].groupby(df['source']).mean()

Yet only gives each source and it's mean, with no column headers.

Thanks in advance!

回答1:

You can use groupby with aggregate:

df = df.groupby('source') \
       .agg({'text':'size', 'sent':'mean'}) \
       .rename(columns={'text':'count','sent':'mean_sent'}) \
       .reset_index()
print (df)
  source  count  mean_sent
0    bar      2      0.415
1    foo      3     -0.500

回答2:

In newer versions of Panda you don't need the rename anymore, just use named parameters:

df = df.groupby('source') \
       .agg(count=('text', 'size'), mean_sent=('sent', 'mean')) \
       .reset_index()

print (df)
  source  count  mean_sent
0    bar      2      0.415
1    foo      3     -0.500

回答3:

Below one should work fine:

df[['source','sent']].groupby('source').agg(['count','mean'])

回答4:

I think this should provide the output that you wanted:

result = pd.DataFrame(df.groupby('source').size())

results['mean_score'] = df.groupby('source').sent.mean()

来源：https://stackoverflow.com/questions/41040132/pandas-groupby-count-and-mean-combined