Change data type of columns in Pandas

I want to convert a table, represented as a list of lists, into a Pandas DataFrame. As an extremely simplified example:

    a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
    df = pd.DataFrame(a)

What is the best way to convert the columns to the appropriate types, in this case columns 2 and 3 into floats? Is there a way to specify the types while converting to DataFrame? Or is it better to create the DataFrame first and then loop through the columns to change the type for each column? Ideally I would like to do this in a dynamic way because there can be hundreds of columns and I don't want to specify exactly which columns are of which type. All I can guarantee is that each columns contains values of the same type.

You have three main options for converting types in pandas.

1. to_numeric()

The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().

This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.

Basic usage

The input to to_numeric() is a Series or a single column of a DataFrame.

    >>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
    >>> s
    0      8
    1      6
    2    7.5
    3      3
    4    0.9
    dtype: object

    >>> pd.to_numeric(s) # convert everything to float values
    0    8.0
    1    6.0
    2    7.5
    3    3.0
    4    0.9
    dtype: float64

As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:

    # convert Series
    my_series = pd.to_numeric(my_series)

    # convert column "a" of a DataFrame
    df["a"] = pd.to_numeric(df["a"])

You can also use it to convert multiple columns of a DataFrame via the apply() method:

    # convert all columns of DataFrame
    df = df.apply(pd.to_numeric) # convert all columns of DataFrame

    # convert just columns "a" and "b"
    df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)

As long as your values can all be converted, that's probably all you need.

Error handling

But what if some values can't be converted to a numeric type?

to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.

Here's an example using a Series of strings s which has the object dtype:

    >>> s = pd.Series(['1', '2', '4.7', 'pandas', '10'])
    >>> s
    0         1
    1         2
    2       4.7
    3    pandas
    4        10
    dtype: object

The default behaviour is to raise if it can't convert a value. In this case, it can't cope with the string 'pandas':

    >>> pd.to_numeric(s) # or pd.to_numeric(s, errors='raise')
    ValueError: Unable to parse string

Rather than fail, we might want 'pandas' to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:

    >>> pd.to_numeric(s, errors='coerce')
    0     1.0
    1     2.0
    2     4.7
    3     NaN
    4    10.0
    dtype: float64

The third option for errors is just to ignore the operation if an invalid value is encountered:

    >>> pd.to_numeric(s, errors='ignore')
    # the original Series is returned untouched

This last option is particularly useful when you want to convert your entire DataFrame, but don't not know which of our columns can be converted reliably to a numeric type. In that case just write:

    df.apply(pd.to_numeric, errors='ignore')

The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.

Downcasting

By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).

That's usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?

to_numeric() gives you the option to downcast to either 'integer', 'signed', 'unsigned', 'float'. Here's an example for a simple series s of integer type:

    >>> s = pd.Series([1, 2, -7])
    >>> s
    0    1
    1    2
    2   -7
    dtype: int64

Downcasting to 'integer' uses the smallest possible integer that can hold the values:

    >>> pd.to_numeric(s, downcast='integer')
    0    1
    1    2
    2   -7
    dtype: int8

Downcasting to 'float' similarly picks a smaller than normal floating type:

    >>> pd.to_numeric(s, downcast='float')
    0    1.0
    1    2.0
    2   -7.0
    dtype: float32

2. astype()

The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It's very versatile in that you can try and go from one type to the any other.

Basic usage

Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).

Call the method on the object you want to convert and astype() will try and convert it for you:

    # convert all DataFrame columns to the int64 dtype
    df = df.astype(int)

    # convert column "a" to int64 dtype and "b" to complex type
    df = df.astype({"a": int, "b": complex})

    # convert Series to float16 type
    s = s.astype(np.float16)

    # convert Series to Python strings
    s = s.astype(str)

    # convert Series to categorical type - see docs for more details
    s = s.astype('category')

Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you'll get an error trying to convert it to an integer.

As of pandas 0.20.0, this error can be suppressed by passing errors='ignore'. Your original object will be return untouched.

Be careful

astype() is powerful, but it will sometimes convert values "incorrectly". For example:

    >>> s = pd.Series([1, 2, -7])
    >>> s
    0    1
    1    2
    2   -7
    dtype: int64

These are small integers, so how about converting to an unsigned 8-bit type to save memory?

    >>> s.astype(np.uint8)
    0      1
    1      2
    2    249
    dtype: uint8

The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!

Trying to downcast using pd.to_numeric(s, downcast='unsigned') instead could help prevent this error.

3. infer_objects()

Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).

For example, here's a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:

    >>> df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
    >>> df.dtypes
    a    object
    b    object
    dtype: object

Using infer_objects(), you can change the type of column 'a' to int64:

    >>> df = df.infer_objects()
    >>> df.dtypes
    a     int64
    b    object
    dtype: object

Column 'b' has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.

From: stackoverflow.com/q/15891038