KeyError: False when I enter conditions in Pandas

November 25, 2021

I’m getting the KeyError: False when I run this line:

df['Eligible'] = df[('DeliveryOnTime' == "On-time") | ('DeliveryOnTime' == "Early")]

I’ve been trying to find a way to execute this condition using np.where and .loc() as well but neither work. Open to other ideas on how to apply the condition to the new column Eligible using data from DeliveryOnTime

I’ve tried these:

np.where

df['Eligible'] = np.where((df['DeliveryOnTime'] == "On-time") | (df['DeliveryOnTime'] == "Early"), 1, 1)

.loc()

df['Eligible'] = df.loc[(df['DeliveryOnTime'] == "On-time") & (df['DeliveryOnTime'] == "Early"), 'Total Orders'].sum()

Sample Data:

data = {'ID': [1, 1, 1, 2, 2, 3, 4, 5, 5],
      'DeliveryOnTime': ["On-time", "Late", "Early", "On-time", "On-time", "Late", "Early", "Early", "On-time"],
     }

df = pd.DataFrame(data)

#For the sake of example data, the count of `DeliveryOnTime` will be the total number of orders. 
df['Total Orders'] = df['DeliveryOnTime'].count()

>Solution :

The right syntax is:

df['Eligible'] = (df['DeliveryOnTime'] == "On-time") | (df['DeliveryOnTime'] == "Early")

# OR

df['Eligible'] = df['DeliveryOnTime'].isin(["On-time", "Early"])

Output:

>>> df
   ID DeliveryOnTime  Total Orders  Eligible
0   1        On-time             9      True
1   1           Late             9     False
2   1          Early             9      True
3   2        On-time             9      True
4   2        On-time             9      True
5   3           Late             9     False
6   4          Early             9      True
7   5          Early             9      True
8   5        On-time             9      True