pandas Data Types Changing dtypes


Example

astype() method changes the dtype of a Series and returns a new Series.

In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 
                           'C': ['1.1.2010', '2.1.2011', '3.1.2011'], 
                           'D': ['1 days', '2 days', '3 days'],
                           'E': ['1', '2', '3']})
In [2]: df
Out[2]:
   A    B          C       D  E
0  1  1.0   1.1.2010  1 days  1
1  2  2.0   2.1.2011  2 days  2
2  3  3.0   3.1.2011  3 days  3

In [3]: df.dtypes
Out[3]:
A      int64
B    float64
C     object
D     object
E     object
dtype: object

Change the type of column A to float, and type of column B to integer:

In [4]: df['A'].astype('float')
Out[4]:
0    1.0
1    2.0
2    3.0
Name: A, dtype: float64

In [5]: df['B'].astype('int')
Out[5]:
0    1
1    2
2    3
Name: B, dtype: int32

astype() method is for specific type conversion (i.e. you can specify .astype(float64'), .astype(float32), or .astype(float16)). For general conversion, you can use pd.to_numeric, pd.to_datetime and pd.to_timedelta.

Changing the type to numeric

pd.to_numeric changes the values to a numeric type.

In [6]: pd.to_numeric(df['E'])
Out[6]:
0    1
1    2
2    3
Name: E, dtype: int64

By default, pd.to_numeric raises an error if an input cannot be converted to a number. You can change that behavior by using the errors parameter.

# Ignore the error, return the original input if it cannot be converted
In [7]: pd.to_numeric(pd.Series(['1', '2', 'a']), errors='ignore')
Out[7]:
0    1
1    2
2    a
dtype: object

# Return NaN when the input cannot be converted to a number
In [8]: pd.to_numeric(pd.Series(['1', '2', 'a']), errors='coerce')
Out[8]:
0    1.0
1    2.0
2    NaN
dtype: float64

If need check all rows with input cannot be converted to numeric use boolean indexing with isnull:

In [9]: df = pd.DataFrame({'A': [1, 'x', 'z'],
                           'B': [1.0, 2.0, 3.0],
                           'C': [True, False, True]})

In [10]: pd.to_numeric(df.A, errors='coerce').isnull()
Out[10]: 
0    False
1     True
2     True
Name: A, dtype: bool

In [11]: df[pd.to_numeric(df.A, errors='coerce').isnull()]
Out[11]: 
   A    B      C
1  x  2.0  False
2  z  3.0   True

Changing the type to datetime

In [12]: pd.to_datetime(df['C'])
Out[12]:
0   2010-01-01
1   2011-02-01
2   2011-03-01
Name: C, dtype: datetime64[ns]

Note that 2.1.2011 is converted to February 1, 2011. If you want January 2, 2011 instead, you need to use the dayfirst parameter.

In [13]: pd.to_datetime('2.1.2011', dayfirst=True)
Out[13]: Timestamp('2011-01-02 00:00:00')

Changing the type to timedelta

In [14]: pd.to_timedelta(df['D'])
Out[14]:
0   1 days
1   2 days
2   3 days
Name: D, dtype: timedelta64[ns]