我如何添加一个颜色列到下面的数据框架,使颜色='绿色'如果设置== 'Z',和颜色='红色'否则?
Type Set
1 A Z
2 B Z
3 B X
4 C Y
我如何添加一个颜色列到下面的数据框架,使颜色='绿色'如果设置== 'Z',和颜色='红色'否则?
Type Set
1 A Z
2 B Z
3 B X
4 C Y
当前回答
列表推导式是有条件地创建另一列的另一种方法。如果您在列中使用对象dtype,就像您的示例一样,列表推导式通常优于大多数其他方法。
示例列表理解:
df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%时间它测试:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
%timeit df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%timeit df['color'] = np.where(df['Set']=='Z', 'green', 'red')
%timeit df['color'] = df.Set.map( lambda x: 'red' if x == 'Z' else 'green')
1000 loops, best of 3: 239 µs per loop
1000 loops, best of 3: 523 µs per loop
1000 loops, best of 3: 263 µs per loop
其他回答
另一种实现这一目标的方法是
df['color'] = df.Set.map( lambda x: 'red' if x == 'Z' else 'green')
使用.apply()方法的一行代码如下:
df['color'] = df['Set'].apply(lambda set_: 'green' if set_=='Z' else 'red')
之后,df数据帧是这样的:
>>> print(df)
Type Set color
0 A Z green
1 B Z green
2 B X red
3 C Y red
如果你在处理海量数据,记忆方法是最好的:
# First create a dictionary of manually stored values
color_dict = {'Z':'red'}
# Second, build a dictionary of "other" values
color_dict_other = {x:'green' for x in df['Set'].unique() if x not in color_dict.keys()}
# Next, merge the two
color_dict.update(color_dict_other)
# Finally, map it to your column
df['color'] = df['Set'].map(color_dict)
当您有许多重复的值时,这种方法将是最快的。我的一般经验法则是记住data_size > 10**4 & n_distinct < data_size/4
在一种情况下,记忆10,000行,不同值不超过2,500。
列表推导式是有条件地创建另一列的另一种方法。如果您在列中使用对象dtype,就像您的示例一样,列表推导式通常优于大多数其他方法。
示例列表理解:
df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%时间它测试:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
%timeit df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%timeit df['color'] = np.where(df['Set']=='Z', 'green', 'red')
%timeit df['color'] = df.Set.map( lambda x: 'red' if x == 'Z' else 'green')
1000 loops, best of 3: 239 µs per loop
1000 loops, best of 3: 523 µs per loop
1000 loops, best of 3: 263 µs per loop
你可以使用pandas方法:
df['color'] = 'green'
df['color'] = df['color'].where(df['Set']=='Z', other='red')
# Replace values where the condition is False
or
df['color'] = 'red'
df['color'] = df['color'].mask(df['Set']=='Z', other='green')
# Replace values where the condition is True
或者,你也可以使用lambda函数的transform方法:
df['color'] = df['Set'].transform(lambda x: 'green' if x == 'Z' else 'red')
输出:
Type Set color
1 A Z green
2 B Z green
3 B X red
4 C Y red
@chai的性能比较:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Type':list('ABBC')*1000000, 'Set':list('ZZXY')*1000000})
%timeit df['color1'] = 'red'; df['color1'].where(df['Set']=='Z','green')
%timeit df['color2'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%timeit df['color3'] = np.where(df['Set']=='Z', 'red', 'green')
%timeit df['color4'] = df.Set.map(lambda x: 'red' if x == 'Z' else 'green')
397 ms ± 101 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
976 ms ± 241 ms per loop
673 ms ± 139 ms per loop
796 ms ± 182 ms per loop