Python3.7pandas1.0.1dataframe-计算一个范围内列的和并重新组合为一个新行?

你可以试试这样的方法:

# sort dataframe
df.sort_values(by=['count'], inplace=True)
# recreate the index of your rows to make sure that 0 corresponds to the one with the higher count
df.reset_index(drop=True, inplace=True)
# add your new row to your dataset
df.append({'useragent': 'Others', 'count': df.loc[5:]['count'].cumsum()}, inplace=True)
# drop the rows you don't need anymore
df.drop([5:len(df.index.values.tolist())-1], inplace=True)

我不完全确定,但值得一试。我希望它能给你一些建议。

使用:

#first sorting data if necessary
df1 = df.sort_values('count', ascending=False)

#then get top 4 rows
df2 = df1.head(4)
#filter column `count` for all values after 4 rows
summed = df1.loc[df1.index[4:], 'count'].sum()

#create DataFrame by another counts
df3 = pd.DataFrame({'useragent':['Other'], 'count':[summed]})

#join together
df4 = pd.concat([df2, df3], sort=False, ignore_index=True)
print (df4)
  useragent  count
0    iPhone  11298
1       Mac   3206
2      iPad    627
3  SM-N960F    433
4     Other    435

#filter by threshold
mask = df['count'] > 500
#filtered rows by boolean indexing
df2 = df[mask]
#inverted mask - sum by count
summed = df.loc[~mask, 'count'].sum()
#same like above
df3 = pd.DataFrame({'useragent':['Other'], 'count':[summed]})

df5 = pd.concat([df2, df3], sort=False, ignore_index=True)
print (df5)
  useragent  count
0    iPhone  11298
1       Mac   3206
2      iPad    627
3     Other    868