Python Pandas - Changing some column types to categories

Question

I have fed the following CSV file into iPython Notebook:

public = pd.read_csv(\"categories.csv\")
public

I\'ve also imported pandas as pd, nump

Answer 1

You can use the pandas.DataFrame.apply method along with a lambda expression to solve this. In your example you could use

df[['parks', 'playgrounds', 'sports']].apply(lambda x: x.astype('category'))

I don't know of a way to execute this inplace, so typically I'll end up with something like this:

df[df.select_dtypes(['object']).columns] = df.select_dtypes(['object']).apply(lambda x: x.astype('category'))

Obviously you can replace .select_dtypes with explicit column names if you don't want to select all of a certain datatype (although in your example it seems like you wanted all object types).

Answer 2

Jupyter Notebook

In my case, I had big Dataframe with many objects that I would like to convert it to category.

Therefore, what I did is I selected the object columns and filled anything that is NA to missing and then saved it in the original Dataframe as in

# Convert Object Columns to Categories
obj_df =df.select_dtypes(include=['object']).copy()
obj_df=obj_df.fillna('Missing')
for col in obj_df:
    obj_df[col] = obj_df[col].astype('category')
df[obj_df.columns]=obj_df[obj_df.columns]
df.head()

I hope this might be a helpful resource for later reference

Answer 3

As of pandas 0.19.0, What's New describes that read_csv supports parsing Categorical columns directly. This answer applies only if you're starting from read_csv otherwise, I think unutbu's answer is still best. Example on 10,000 records:

import pandas as pd
import numpy as np

# Generate random data, four category-like columns, two int columns
N=10000
categories = pd.DataFrame({
            'parks' : np.random.choice(['strongly agree','agree', 'disagree'], size=N),
            'playgrounds' : np.random.choice(['strongly agree','agree', 'disagree'], size=N),
            'sports' : np.random.choice(['important', 'very important', 'not important'], size=N),
            'roading' : np.random.choice(['important', 'very important', 'not important'], size=N),
            'resident' : np.random.choice([1, 2, 3], size=N),
            'children' : np.random.choice([0, 1, 2, 3], size=N)
                       })
categories.to_csv('categories_large.csv', index=False)

<0.19.0 (or >=19.0 without specifying dtype)

pd.read_csv('categories_large.csv').dtypes # inspect default dtypes

children        int64
parks          object
playgrounds    object
resident        int64
roading        object
sports         object
dtype: object

>=0.19.0

For mixed dtypes parsing as Categorical can be implemented by passing a dictionary dtype={'colname' : 'category', ...} in read_csv.

pd.read_csv('categories_large.csv', dtype={'parks': 'category',
                                           'playgrounds': 'category',
                                           'sports': 'category',
                                           'roading': 'category'}).dtypes
children          int64
parks          category
playgrounds    category
resident          int64
roading        category
sports         category
dtype: object

Performance

A slight speed-up (local jupyter notebook), as mentioned in the release notes.

# unutbu's answer
%%timeit
public = pd.read_csv('categories_large.csv')
for col in ['parks', 'playgrounds', 'sports', 'roading']:
    public[col] = public[col].astype('category')
10 loops, best of 3: 20.1 ms per loop

# parsed during read_csv
%%timeit
category_cols = {item: 'category' for item in ['parks', 'playgrounds', 'sports', 'roading']}
public = pd.read_csv('categories_large.csv', dtype=category_cols)
100 loops, best of 3: 14.3 ms per loop

Answer 4

I found that using a for loop works well.

for col in ['col_variable_name_1', 'col_variable_name_2', ect..]:
    dataframe_name[col] = dataframe_name[col].astype(float)

Answer 5

Sometimes, you just have to use a for-loop:

for col in ['parks', 'playgrounds', 'sports', 'roading']:
    public[col] = public[col].astype('category')

Answer 6

To make things easier. No apply. No map. No loop.

cols=data.select_dtypes(exclude='int').columns.to_list()
data[cols]=data[cols].astype('category')