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import numpy as np    
df['Publisher'] = np.where(df["Counts"] <= 5, "Other", df['Publisher'])

可以使用.loc[]索引器有选择地将掩码仅应用于Publisher列。

publisher_subset = data.filter(['Publisher'])
df = publisher_subset.value_counts().reset_index(name='Counts')

df.loc[df["Counts"] <= 5, "Publisher"] = "Other"

如果仅选择Publisher列，则可以使用mask：

# Select publisher column only to mask values
rename_others = lambda x: x['Publisher'].mask(x['counts'] <= 5, other='Others')

out = (df.value_counts('Publisher').reset_index(name='counts')
         .assign(Publisher=rename_others))
print(out)

# Output
  Publisher  counts
0      Sony       7
1    Bandai       6
2    Others       5
3    Others       5
4    Others       5
5    Others       4
6    Others       4

我想你也想用Publisher求和：

out = (df.value_counts('Publisher').reset_index(name='counts')
         .assign(Publisher=rename_others)
         .groupby('Publisher', sort=False, as_index=False).sum())

  Publisher  counts
0      Sony       7
1    Bandai       6
2    Others      23