I created a DataFrame from a list of lists:
table = [
['a', '1.2', '4.2' ],
['b', '70', '0.03'],
['x', '5', '0' ],
]
df = pd.DataFrame(table)
How do I convert the columns to specific types? In this case, I want to convert columns 2 and 3 into floats.
Is there a way to specify the types while converting the list to DataFrame? Or is it better to create the DataFrame first and then loop through the columns to change the dtype 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 column contains values of the same type.
Best Answer
You have four main options for converting types in pandas:
to_numeric()
- provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See alsoto_datetime()
andto_timedelta()
.)astype()
- convert (almost) any type to (almost) any other type (even if it's not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).infer_objects()
- a utility method to convert object columns holding Python objects to a pandas type if possible.convert_dtypes()
- convert DataFrame columns to the "best possible" dtype that supportspd.NA
(pandas' object to indicate a missing value).Read on for more detailed explanations and usage of each of these methods.
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.As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
You can also use it to convert multiple columns of a DataFrame via the
apply()
method: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 anerrors
keyword argument that allows you to force non-numeric values to beNaN
, or simply ignore columns containing these values.Here's an example using a Series of strings
s
which has the object dtype:The default behaviour is to raise if it can't convert a value. In this case, it can't cope with the string 'pandas':
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 theerrors
keyword argument:The third option for
errors
is just to ignore the operation if an invalid value is encountered:This last option is particularly useful for converting your entire DataFrame, but don't know which of our columns can be converted reliably to a numeric type. In that case, just write:
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 anint64
orfloat64
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
, orint8
?to_numeric()
gives you the option to downcast to either'integer'
,'signed'
,'unsigned'
,'float'
. Here's an example for a simple seriess
of integer type:Downcasting to
'integer'
uses the smallest possible integer that can hold the values:Downcasting to
'float'
similarly picks a smaller than normal floating type: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 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: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 aNaN
orinf
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 returned untouched.Be careful
astype()
is powerful, but it will sometimes convert values "incorrectly". For example:These are small integers, so how about converting to an unsigned 8-bit type to save memory?
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:
Using
infer_objects()
, you can change the type of column 'a' to int64:Column 'b' has been left alone since its values were strings, not integers. If you wanted to force both columns to an integer type, you could use
df.astype(int)
instead.4.
convert_dtypes()
Version 1.0 and above includes a method
convert_dtypes()
to convert Series and DataFrame columns to the best possible dtype that supports thepd.NA
missing value.Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type, if all of the values are integers (or missing values): an object column of Python integer objects are converted to
Int64
, a column of NumPyint32
values, will become the pandas dtypeInt32
.With our
object
DataFramedf
, we get the following result:Since column 'a' held integer values, it was converted to the
Int64
type (which is capable of holding missing values, unlikeint64
).Column 'b' contained string objects, so was changed to pandas'
string
dtype.By default, this method will infer the type from object values in each column. We can change this by passing
infer_objects=False
:Now column 'a' remained an object column: pandas knows it can be described as an 'integer' column (internally it ran
infer_dtype
) but didn't infer exactly what dtype of integer it should have so did not convert it. Column 'b' was again converted to 'string' dtype as it was recognised as holding 'string' values.