Question

I have a dataframe like below.

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.

i need to compare score and height columns with trigger 1 -3 columns.

Flag Column:

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

How to write if else conditions in pandas dataframe and derive columns?

Expected Output

  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?

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

Answer 1

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

注意：您可以使用非常嵌套的np.where来执行此操作，但我更喜欢将一个函数应用于多个if-else

Answer 2

you can use also apply with a custom function on axis 1 like this :

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 :

Answer 3

这是一种使用numpy.select()的方式，以一种简洁的方式进行操作：

conditions = [
    (df2['trigger1'] <= df2['score']) & (df2['score'] < df2['trigger2']) & (df2['height'] < 8),
    (df2['trigger2'] <= df2['score']) & (df2['score'] < df2['trigger3']) & (df2['height'] < 8),
    (df2['trigger3'] <= df2['score']) & (df2['height'] < 8),
    (df2['height'] > 8)
]

choices = ['Red','Yellow','Orange', np.nan]
df['Flag1'] = np.select(conditions, choices, default=np.nan)

multiple if else conditions in pandas dataframe and derive multiple columns

3 个答案: