There are 51 columns in my .csv file, I need to convert all int 64 data types to categorical in one go.How can I do that? Do I need to mention all the column names in data[].
data[].astype('categorical')
Fortunately, the python tools of pandas and scikit-learn provide several approaches that can be applied to transform the categorical data into suitable numeric values.
Categorical function is used to convert / typecast integer or character column to categorical in pandas python. Typecast a numeric column to categorical using categorical function ().
For example, We will take a dataset of people’s salaries based on their level of education. This is an ordinal type of categorical variable. We will convert their education levels into numeric terms. replace (to_replace=None, value=None, inplace=False, limit=None, regex=False, method=’pad’)
Guide to Encoding Categorical Values in Python Introduction The Data Set Approach #1 - Find and Replace Approach #2 - Label Encoding Approach #3 - One Hot Encoding Approach #4 - Custom Binary Encoding Scikit-Learn Advanced Approaches Conclusion
You can get the column names into a list, then loop to change the type of each column.
import pandas as pd
import numpy as np
# create example dataframe
cats = ['A', 'B', 'C', 'D', 'E']
int_matrix = np.random.randint(10, size=(7,5))
df = pd.DataFrame(data = int_matrix, columns=cats)
print("Original example data\n")
print(df)
print(df.dtypes)
# get column names of data frame in a list
col_names = list(df)
print("\nNames of dataframe columns")
print(col_names)
# loop to change each column to category type
for col in col_names:
df[col] = df[col].astype('category',copy=False)
print("\nExample data changed to category type")
print(df)
print(df.dtypes)
The output of this little program is:
Original example data
A B C D E
0 0 4 9 2 9
1 2 5 2 4 1
2 1 1 0 5 7
3 1 2 5 4 0
4 9 2 6 5 3
5 3 3 2 1 7
6 6 0 8 7 3
A int32
B int32
C int32
D int32
E int32
dtype: object
Names of dataframe columns
['A', 'B', 'C', 'D', 'E']
Example data changed to category type
A B C D E
0 0 4 9 2 9
1 2 5 2 4 1
2 1 1 0 5 7
3 1 2 5 4 0
4 9 2 6 5 3
5 3 3 2 1 7
6 6 0 8 7 3
A category
B category
C category
D category
E category
dtype: object
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