Pandas series.map 将值更改为 NaN

Question

I am working on an SMS dataset that has two columns a "label column" which is consists of "ham/spam" and another column with "messages" consist of a bunch of strings.

I converted the "Label column" to numeric labels, ham=1, and spam=0

#Converting our labels to numeric labels
# ham = 0 and spam = 1
dfcat = dataset['label']=dataset.label.map({'ham':1,'spam':0})
dfcat.head()

so when I run the above code the first time it gave me the exact thing am looking for but after I ran it again it started giving me "Nan".

Out[108]: 
0   NaN
1   NaN
2   NaN
3   NaN
4   NaN
Name: label, dtype: float64

Please, I need a way to fix this.

Answer 1

@G. Anderson gave the reason why are you seeing those NaN the second time you rerun it.

As for a way to handle categorical variables in Python, one could use one hot encoding . Toy example below:

import pandas as pd

df = pd.DataFrame({"col1": ["a", "b", "c"], "label": ["ham", "spam", "ham"]})
df_ohe = pd.get_dummies(df, prefix="ohe", drop_first=True, columns=["label"])
df_ohe

However, it also depends on the amount of categorical variables and their cardinality (if high, one hot encoding might not be the best approach).

Answer 2

The behavior of the series.map() function is to replace the values in the provided dictionary, and change other values to NaN . If you want to run the same line of code multiple times, then all values need to be accounted for. You can either use a defaultdict, which allows a default value to be set, or just include the result of the first run as inputs in case you run it a second time. Change

dfcat = dataset['label']=dataset.label.map({'ham':1,'spam':0})

to

dfcat = dataset['label']=dataset.label.map({'ham':1,'spam':0,1:1,0:0})