pandas 根据其他列的值创建新列
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Creating a new column based on the values of other columns
提问by Mike
I wanted to create a "High Value Indicator" column, which says "Y" or "N" based on two different value columns. I want the new column to have a "Y" when Value_1 is > 1,000 or Value_2 > 15,000. Bellow is the table, the desired output would include the indicator column based on the or condition about.
我想创建一个“高价值指标”列，它根据两个不同的值列显示“Y”或“N”。当 Value_1 > 1,000 或 Value_2 > 15,000 时，我希望新列有一个“Y”。下面是表格，所需的输出将包括基于或条件的指标列。
ID Value_1 Value_2
1 100 2500
2 250 6250
3 625 15625
4 1500 37500
5 3750 93750
回答by jezrael
Use numpy.wherewith chained conditions by |for or:
使用numpy.where由链式条件|为or：
df['High Value Indicator'] = np.where((df.Value_1 > 1000) | (df.Value_2 > 15000), 'Y', 'N')
Or mapby dictionary:
或map通过dictionary：
df['High Value Indicator'] = ((df.Value_1 > 1000) | (df.Value_2 > 15000))
.map({True:'Y', False:'N'})
print (df)
ID Value_1 Value_2 High Value Indicator
0 1 100 2500 N
1 2 250 6250 N
2 3 625 15625 Y
3 4 1500 37500 Y
4 5 3750 93750 Y
Timings:
时间：
df = pd.concat([df] * 10000, ignore_index=True)
In [76]: %timeit df['High Value Indicator1'] = np.where((df.Value_1 > 1000) | (df.Value_2 > 15000), 'Y', 'N')
100 loops, best of 3: 4.03 ms per loop
In [77]: %timeit df['High Value Indicator2'] = ((df.Value_1 > 1000) | (df.Value_2 > 15000)).map({True:'Y', False:'N'})
100 loops, best of 3: 4.82 ms per loop
In [78]: %%timeit
...: df.loc[((df['Value_1'] > 1000)
...: |(df['Value_2'] > 15000)), 'High_Value_Ind3'] = 'Y'
...:
...: df['High_Value_Ind3'] = df['High_Value_Ind3'].fillna('N')
...:
100 loops, best of 3: 5.28 ms per loop
In [79]: %timeit df['High Value Indicator'] = (df.apply(lambda x: 'Y' if (x.Value_1>1000 or x.Value_2>15000) else 'N', axis=1))
1 loop, best of 3: 1.72 s per loop
回答by Brian
Try using .loc and .fillna
尝试使用 .loc 和 .fillna
df.loc[((df['Value_1'] > 1000)
|(df['Value_2'] > 15000)), 'High_Value_Ind'] = 'Y'
df['High_Value_Ind'] = df['High_Value_Ind'].fillna('N')
回答by YOBEN_S
Using map
使用 map
df['High Value Indicator'] =((df.Value_1 > 1000) | (df.Value_2 > 15000)).map({True:'Y',False:'N'})
df
Out[849]:
ID Value_1 Value_2 High Value Indicator
0 1 100 2500 N
1 2 250 6250 N
2 3 625 15625 Y
3 4 1500 37500 Y
4 5 3750 93750 Y
回答by Allen
You can also use apply:
您还可以使用申请：
df['High Value Indicator'] = (
df.apply(lambda x: 'Y' if (x.Value_1>1000 or x.Value_2>15000) else 'N', axis=1)
)
