python - Pandas 中的过滤和替换子字符串

Question

如何过滤 name 包含 Al 的 df 行，并将 large 替换为 L 在运动中？

可重现的示例:

df = pd.DataFrame({'name': ['Bob', 'Jane', 'Alice'], 
                   'sport': ['tennis small', 'football medium', 'basketball large']})

预期输出:

    name    sport
0   Bob     tennis small
1   Jane    football medium
2   Alice   basketball L

Answer 1

仅过滤必要的行并为它们使用 Series.str.replace ，像替换所有列而不进行过滤这样的性能更好:

m = df['name'].str.contains('Al', na=False)
df.loc[m, 'sport'] = df.loc[m, 'sport'].str.replace('large', 'L', regex=True)
print (df)
    name            sport
0    Bob     tennis small
1   Jane  football medium
2  Alice     basketball L

---

#100 matched values from 30k
df = pd.DataFrame({'name': ['Bob','Jane','alice'] * 9900 + ['Bob', 'Jane', 'Alice'] * 100, 
                   'sport': ['tennis small','football medium', 'basketball large'] * 10000})

print (df)
        name             sport
0        Bob      tennis small
1       Jane   football medium
2      alice  basketball large
3        Bob      tennis small
4       Jane   football medium
     ...               ...
29995   Jane   football medium
29996  Alice  basketball large
29997    Bob      tennis small
29998   Jane   football medium
29999  Alice  basketball large

[30000 rows x 2 columns]

In [76]: %%timeit
    ...: m = df['name'].str.contains('Al')
    ...: df.loc[m, 'sport'] = df.loc[m, 'sport'].str.replace('large', 'L', regex=True)
    ...: 
    ...: 
14.6 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [77]: %%timeit
    ...: df.loc[df.name.str.contains('Al'), 'sport'] = df.sport.str.replace('large', 'L')
    ...: 
    ...: 
34.8 ms ± 254 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [78]: %%timeit
    ...: df['sport'] = np.where(df['name'].str.contains('Al'), df['sport'].str.replace('large', 'L', regex=True),  df['sport'])
    ...: 
    ...: 
35 ms ± 1.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

---

#10k matched values from 30k
df = pd.DataFrame({'name': ['Bob', 'Jane','Alice'] * 10000, 
                   'sport': ['tennis small', 'football medium', 'basketball large'] * 10000})



print (df)


In [80]: %%timeit
    ...: m = df['name'].str.contains('Al')
    ...: df.loc[m, 'sport'] = df.loc[m, 'sport'].str.replace('large', 'L', regex=True)
    ...: 
    ...: 
22.2 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [81]: %%timeit
    ...: df.loc[df.name.str.contains('Al'), 'sport'] = df.sport.str.replace('large', 'L')
    ...: 
    ...: 
34 ms ± 118 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [82]: %%timeit
    ...: df['sport'] = np.where(df['name'].str.contains('Al'), df['sport'].str.replace('large', 'L', regex=True),  df['sport'])
    ...: 
    ...: 
34.9 ms ± 2.69 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)