Python 熊猫数据框中的多个 if else 条件并派生多个列
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multiple if else conditions in pandas dataframe and derive multiple columns
提问by Kumar AK
I have a dataframe like below.
我有一个如下所示的数据框。
import pandas as pd
import numpy as np
raw_data = {'student':['A','B','C','D','E'],
'score': [100, 96, 80, 105,156],
'height': [7, 4,9,5,3],
'trigger1' : [84,95,15,78,16],
'trigger2' : [99,110,30,93,31],
'trigger3' : [114,125,45,108,46]}
df2 = pd.DataFrame(raw_data, columns = ['student','score', 'height','trigger1','trigger2','trigger3'])
print(df2)
I need to derive Flag column based on multiple conditions.
我需要根据多个条件派生 Flag 列。
i need to compare score and height columns with trigger 1 -3 columns.
我需要将分数和高度列与触发器 1 -3 列进行比较。
Flag Column:
标志栏：
if Score greater than equal trigger 1 and height less than 8 then Red --
if Score greater than equal trigger 2 and height less than 8 then Yellow --
if Score greater than equal trigger 3 and height less than 8 then Orange --
if height greater than 8 then leave it as blank
如果分数大于等于触发 1 且高度小于 8，则为红色 -
如果分数大于等于触发器 2 且高度小于 8，则为黄色 -
如果分数大于等于触发 3 且高度小于 8，则为橙色——
如果高度大于 8，则将其留空
How to write if else conditions in pandas dataframe and derive columns?
如何在熊猫数据框中编写 if else 条件并派生列？
Expected Output
预期产出
student score height trigger1 trigger2 trigger3 Flag
0 A 100 7 84 99 114 Yellow
1 B 96 4 95 110 125 Red
2 C 80 9 15 30 45 NaN
3 D 105 5 78 93 108 Yellow
4 E 156 3 16 31 46 Orange
For other column Text1 in my original question i have tired this one but the interger columns not converting the string when concatenation using astype(str) any other approach?
对于我原来的问题中的其他列 Text1 我已经厌倦了这个，但是在使用 astype(str) 任何其他方法连接时，整数列没有转换字符串？
def text_df(df):
if (df['trigger1'] <= df['score'] < df['trigger2']) and (df['height'] < 8):
return df['student'] + " score " + df['score'].astype(str) + " greater than " + df['trigger1'].astype(str) + " and less than height 5"
elif (df['trigger2'] <= df['score'] < df['trigger3']) and (df['height'] < 8):
return df['student'] + " score " + df['score'].astype(str) + " greater than " + df['trigger2'].astype(str) + " and less than height 5"
elif (df['trigger3'] <= df['score']) and (df['height'] < 8):
return df['student'] + " score " + df['score'].astype(str) + " greater than " + df['trigger3'].astype(str) + " and less than height 5"
elif (df['height'] > 8):
return np.nan
回答by Vaishali
You need chained comparison using upper and lower bound
您需要使用上限和下限进行链式比较
def flag_df(df):
if (df['trigger1'] <= df['score'] < df['trigger2']) and (df['height'] < 8):
return 'Red'
elif (df['trigger2'] <= df['score'] < df['trigger3']) and (df['height'] < 8):
return 'Yellow'
elif (df['trigger3'] <= df['score']) and (df['height'] < 8):
return 'Orange'
elif (df['height'] > 8):
return np.nan
df2['Flag'] = df2.apply(flag_df, axis = 1)
student score height trigger1 trigger2 trigger3 Flag
0 A 100 7 84 99 114 Yellow
1 B 96 4 95 110 125 Red
2 C 80 9 15 30 45 NaN
3 D 105 5 78 93 108 Yellow
4 E 156 3 16 31 46 Orange
Note: You can do this with a very nested np.where but I prefer to apply a function for multiple if-else
注意：你可以用一个非常嵌套的 np.where 来做到这一点，但我更喜欢为多个 if-else 应用一个函数
回答by Esptheitroad Murhabazi
you can use also apply with a custom function on axis 1 like this :
您还可以像这样在轴 1 上使用自定义函数 apply ：
def color_selector(x):
if (x['trigger1'] <= x['score'] < x['trigger2']) and (x['height'] < 8):
return 'Red'
elif (x['trigger2'] <= x['score'] < x['trigger3']) and (x['height'] < 8):
return 'Yellow'
elif (x['trigger3'] <= x['score']) and (x['height'] < 8):
return 'Orange'
elif (x['height'] > 8):
return ''
df2 = df2.assign(flag=df2.apply(color_selector, axis=1))
you will get something like this :
你会得到这样的东西：
