In [1]:

    

import numpy as np;
import pandas as pd


    

In [2]:

    

dtypes = ['int64', 'int8', 'float64', 'datetime64[ns]', 'timedelta64[ns]','complex128', 'object', 'bool']


    

In [3]:

    

n = 5000
data = dict([ (t, np.random.randint(100, size=n).astype(t)) for t in dtypes])
df = pd.DataFrame(data)


    

In [4]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 8 columns):
bool               5000 non-null bool
complex128         5000 non-null complex128
datetime64[ns]     5000 non-null datetime64[ns]
float64            5000 non-null float64
int64              5000 non-null int64
int8               5000 non-null int8
object             5000 non-null object
timedelta64[ns]    5000 non-null timedelta64[ns]
dtypes: bool(1), complex128(1), datetime64[ns](1), float64(1), int64(1), int8(1), object(1), timedelta64[ns](1)
memory usage: 283.3+ KB

In [5]:

    

pd.DataFrame(df.int8).info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 1 columns):
int8    5000 non-null int8
dtypes: int8(1)
memory usage: 5.0 KB

In [6]:

    

pd.DataFrame(df.int64).info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 1 columns):
int64    5000 non-null int64
dtypes: int64(1)
memory usage: 39.1 KB

In [7]:

    

df['categorical'] = df['object'].astype('category')


    

In [8]:

    

pd.DataFrame(df.categorical).info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 1 columns):
categorical    5000 non-null category
dtypes: category(1)
memory usage: 5.7 KB

In [9]:

    

pd.DataFrame(df.object).info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 1 columns):
object    5000 non-null object
dtypes: object(1)
memory usage: 39.1+ KB

In [10]:

    

pd.DataFrame(df.categorical).head()


    

    
Out[10]:






  

    

      

      
categorical
    
  
  

    

      
0
      
36
    
    

      
1
      
26
    
    

      
2
      
76
    
    

      
3
      
51
    
    

      
4
      
74
    
  

In [11]:

    

pd.DataFrame(df.object).head()


    

    
Out[11]:






  

    

      

      
object
    
  
  

    

      
0
      
36
    
    

      
1
      
26
    
    

      
2
      
76
    
    

      
3
      
51
    
    

      
4
      
74
    
  

In [12]:

    

df['bool'].head()


    

    
Out[12]:





0    True
1    True
2    True
3    True
4    True
Name: bool, dtype: bool

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