Python 具有多个条件的 Numpy“where”
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Numpy "where" with multiple conditions
提问by Poisson
I try to add a new column "energy_class" to a dataframe "df_energy" which it contains the string "high" if the "consumption_energy" value > 400, "medium" if the "consumption_energy" value is between 200 and 400, and "low" if the "consumption_energy" value is under 200.
I try to use np.where from numpy, but I see that numpy.where(condition[, x, y])treat only two condition not 3 like in my case.
我尝试将一个新列“energy_class”添加到数据框“df_energy”中，如果“consumption_energy”值> 400，则它包含字符串“high”，如果“consumption_energy”值在200到400之间，则为“medium”，并且“如果“consumption_energy”值低于 200，则“低”。我尝试使用 numpy 中的 np.where，但我看到numpy.where(condition[, x, y])只处理两种情况，而不是像我的情况那样处理 3。
Any idea to help me please?
有什么想法可以帮助我吗？
Thank you in advance
先感谢您
回答by Alexander
回答by Merlin
Try this: Using the setup from @Maxu
试试这个：使用@Maxu 的设置
col = 'consumption_energy'
conditions = [ df2[col] >= 400, (df2[col] < 400) & (df2[col]> 200), df2[col] <= 200 ]
choices = [ "high", 'medium', 'low' ]
df2["energy_class"] = np.select(conditions, choices, default=np.nan)
consumption_energy energy_class
0 459 high
1 416 high
2 186 low
3 250 medium
4 411 high
5 210 medium
6 343 medium
7 328 medium
8 208 medium
9 223 medium
回答by MaxU
I would use the cut()method here, which will generate very efficient and memory-saving categorydtype:
我将在这里使用cut()方法，它将生成非常高效且节省内存的categorydtype：
In [124]: df
Out[124]:
consumption_energy
0 459
1 416
2 186
3 250
4 411
5 210
6 343
7 328
8 208
9 223
In [125]: pd.cut(df.consumption_energy,
[0, 200, 400, np.inf],
labels=['low','medium','high']
)
Out[125]:
0 high
1 high
2 low
3 medium
4 high
5 medium
6 medium
7 medium
8 medium
9 medium
Name: consumption_energy, dtype: category
Categories (3, object): [low < medium < high]
回答by MaxU
I like to keep the code clean. That's why I prefer np.vectorizefor such tasks.
我喜欢保持代码干净。这就是为什么我更喜欢np.vectorize这样的任务。
def conditions(x):
if x > 400:
return "High"
elif x > 200:
return "Medium"
else:
return "Low"
func = np.vectorize(conditions)
energy_class = func(df_energy["consumption_energy"])
Then just add numpy array as a column in your dataframe using:
然后只需使用以下方法将 numpy 数组添加为数据框中的列：
df_energy["energy_class"] = energy_class
The advantage in this approach is that if you wish to add more complicated constraints to a column, it can be done easily. Hope it helps.
这种方法的优点是，如果您希望向列添加更复杂的约束，则可以轻松完成。希望能帮助到你。
回答by wpmoradi
I second using np.vectorize. It is much faster than np.where and also cleaner code wise. You can definitely tell the speed up with larger data sets. You can use a dictionary format for your conditionals as well as the output of those conditions.
我第二次使用 np.vectorize。它比 np.where 快得多，而且代码更清晰。您绝对可以通过更大的数据集来判断速度的提高。您可以为您的条件以及这些条件的输出使用字典格式。
# Vectorizing with numpy
row_dic = {'Condition1':'high',
'Condition2':'medium',
'Condition3':'low',
'Condition4':'lowest'}
def Conditions(dfSeries_element,dictionary):
'''
dfSeries_element is an element from df_series
dictionary: is the dictionary of your conditions with their outcome
'''
if dfSeries_element in dictionary.keys():
return dictionary[dfSeries]
def VectorizeConditions():
func = np.vectorize(Conditions)
result_vector = func(df['Series'],row_dic)
df['new_Series'] = result_vector
# running the below function will apply multi conditional formatting to your df
VectorizeConditions()
回答by Poudel
WARNING: Always be careful that if your data has missing values np.wheremay be tricky to use and may give you the wrong result inadvertently.
警告：始终要小心，如果您的数据缺少值np.where，则使用起来可能会很棘手，并且可能会在不经意间给您错误的结果。
Consider this situation:
考虑这种情况：
df['cons_ener_cat'] = np.where(df.consumption_energy > 400, 'high',
(np.where(df.consumption_energy < 200, 'low', 'medium')))
# if we do not use this second line, then
# if consumption energy is missing it would be shown medium, which is WRONG.
df.loc[df.consumption_energy.isnull(), 'cons_ener_cat'] = np.nan
Alternatively, you can use one-more nested np.wherefor medium versus nan which would be ugly.
或者，您可以np.where对 medium 和 nan使用多个嵌套，这会很丑陋。
IMHO best way to go is pd.cut. It deals with NaNs and easy to use.
恕我直言，最好的方法是pd.cut。它处理 NaN 并且易于使用。
Examples:
例子：
import numpy as np
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
# pd.cut
df['age_cat'] = pd.cut(df.age, [0, 20, 60, np.inf], labels=['child','medium','old'])
# manually add another line for nans
df['age_cat2'] = np.where(df.age > 60, 'old', (np.where(df.age <20, 'child', 'medium')))
df.loc[df.age.isnull(), 'age_cat'] = np.nan
# multiple nested where
df['age_cat3'] = np.where(df.age > 60, 'old',
(np.where(df.age <20, 'child',
np.where(df.age.isnull(), np.nan, 'medium'))))
# outptus
print(df[['age','age_cat','age_cat2','age_cat3']].head(7))
age age_cat age_cat2 age_cat3
0 22.0 medium medium medium
1 38.0 medium medium medium
2 26.0 medium medium medium
3 35.0 medium medium medium
4 35.0 medium medium medium
5 NaN NaN medium nan
6 54.0 medium medium medium
