Change column type in pandas

Question

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\', \'         


        

Answer 1

pandas >= 1.0

Here's a chart that summarises some of the most important conversions in pandas.

Conversions to string are trivial .astype(str) and are not shown in the figure.

"Hard" versus "Soft" conversions

Note that "conversions" in this context could either refer to converting text data into their actual data type (hard conversion), or inferring more appropriate data types for data in object columns (soft conversion). To illustrate the difference, take a look at

df = pd.DataFrame({'a': ['1', '2', '3'], 'b': [4, 5, 6]}, dtype=object)
df.dtypes                                                                  

a    object
b    object
dtype: object

# Actually converts string to numeric - hard conversion
df.apply(pd.to_numeric).dtypes                                             

a    int64
b    int64
dtype: object

# Infers better data types for object data - soft conversion
df.infer_objects().dtypes                                                  

a    object  # no change
b     int64
dtype: object

# Same as infer_objects, but converts to equivalent ExtensionType
df.convert_dtypes().dtypes