python Pandas替换字符串中的单词

Question

Given a dataframe like:

A    B    C
1    a    yes
2    b    yes
3    a    no

I would like to change the dataframe to:

A    B    C
1    a    yes
2    b    no
3    a    no

which means that if column B has the value 'b', I want to change the column C to 'no'. Which can be represented by df[df['B']=='b']['C'].str.replace('yes','no') . But use this will not change dataframe df itself. Even I tried df[df['B']=='b']['C'] = df[df['B']=='b']['C'].str.replace('yes','no') it didn't work. I am wondering how to solve this problem.

Answer 1

Solutions with set values by mask :

df.loc[df.B == 'b', 'C'] = 'no'
print (df)
   A  B    C
0  1  a  yes
1  2  b   no
2  3  a   no

df['C'] = df['C'].mask(df.B == 'b','no')
print (df)
   A  B    C
0  1  a  yes
1  2  b   no
2  3  a   no

Solutions with replace only yes string:

df.loc[df.B == 'b', 'C'] = df['C'].replace('yes', 'no')
print (df)
   A  B    C
0  1  a  yes
1  2  b   no
2  3  a   no

df['C'] = df['C'].mask(df.B == 'b', df['C'].replace('yes', 'no'))
print (df)
   A  B    C
0  1  a  yes
1  2  b   no
2  3  a   no

Difference better seen in changed df :

print (df)
   A  B        C
0  1  a      yes
1  2  b      yes
2  3  b  another
3  4  a       no

df['C_set'] = df['C'].mask(df.B == 'b','no')
df['C_replace'] = df['C'].mask(df.B == 'b', df['C'].replace('yes', 'no'))

print (df)
   A  B        C C_set C_replace
0  1  a      yes   yes       yes
1  2  b      yes    no        no
2  3  b  another    no   another
3  4  a       no    no        no

EDIT:

In your solution is necessary only add loc :

df.loc[df['B']=='b', 'C'] = df.loc[df['B']=='b', 'C'].str.replace('yes','no')
print (df)
   A  B        C
0  1  a      yes
1  2  b       no
2  3  b  another
3  4  a       no

EDIT1:

I was really curious what method is fastest:

#[40000 rows x 3 columns]
df = pd.concat([df]*10000).reset_index(drop=True)    
print (df)

In [37]: %timeit df.loc[df['B']=='b', 'C'] = df['C'].str.replace('yes','no')
10 loops, best of 3: 79.5 ms per loop

In [38]: %timeit df.loc[df['B']=='b', 'C'] = df.loc[df['B']=='b','C'].str.replace('yes','no')
10 loops, best of 3: 48.4 ms per loop

In [39]: %timeit df.loc[df['B']=='b', 'C'] = df.loc[df['B']=='b', 'C'].replace('yes','no')
100 loops, best of 3: 14.1 ms per loop

In [40]: %timeit df['C'] = df['C'].mask(df.B == 'b', df['C'].replace('yes', 'no'))
100 loops, best of 3: 10.1 ms per loop

# piRSquared solution with replace
In [53]: %timeit df.C = np.where(df.B.values == 'b', df.C.replace('yes', 'no'), df.C.values)
100 loops, best of 3: 4.74 ms per loop

EDIT1:

Better is change condition - add df.C == 'yes' or df.C.values == 'yes' if need fastest solution:

df.loc[(df.B == 'b') & (df.C == 'yes'), 'C'] = 'no'

df.C = np.where((df.B.values == 'b') & (df.C.values == 'yes'), 'no', df.C.values)

Answer 2

np.where

df.C = np.where(df.B == 'b', 'no', df.C)

Or

df.C = np.where(df.B.values == 'b', 'no', df.C.values)

pd.Series.mask

df.C = df.C.mask(df.B == 'b', 'no')

---

All change df in place and yield

   A  B    C
0  1  a  yes
1  2  b   no
2  3  a   no

---

timing