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', '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.

Solution 1

You have four main options for converting types in pandas:

1. to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)

2. 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).

3. infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.

4. convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.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.

>>> 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 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:

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 an 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 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 returned 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 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 the pd.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 NumPy int32 values, will become the pandas dtype Int32.

With our object DataFrame df, we get the following result:

>>> df.convert_dtypes().dtypes                                             
a     Int64
b    string
dtype: object

Since column 'a' held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).

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:

>>> df.convert_dtypes(infer_objects=False).dtypes                          
a    object
b    string
dtype: object

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.

Solution 2

How about this?

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['one', 'two', 'three'])
df
Out[16]: 
  one  two three
0   a  1.2   4.2
1   b   70  0.03
2   x    5     0

df.dtypes
Out[17]: 
one      object
two      object
three    object

df[['two', 'three']] = df[['two', 'three']].astype(float)

df.dtypes
Out[19]: 
one       object
two      float64
three    float64

Solution 3

this below code will change datatype of column.

df[['col.name1', 'col.name2'...]] = df[['col.name1', 'col.name2'..]].astype('data_type')

in place of data type you can give your datatype .what do you want like str,float,int etc.

Solution 4

When I've only needed to specify specific columns, and I want to be explicit, I've used (per DOCS LOCATION):

dataframe = dataframe.astype({'col_name_1':'int','col_name_2':'float64', etc. ...})

So, using the original question, but providing column names to it ...

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['col_name_1', 'col_name_2', 'col_name_3'])
df = df.astype({'col_name_2':'float64', 'col_name_3':'float64'})

Solution 5

Here is a function that takes as its arguments a DataFrame and a list of columns and coerces all data in the columns to numbers.

# df is the DataFrame, and column_list is a list of columns as strings (e.g ["col1","col2","col3"])
# dependencies: pandas

def coerce_df_columns_to_numeric(df, column_list):
    df[column_list] = df[column_list].apply(pd.to_numeric, errors='coerce')

So, for your example:

import pandas as pd

def coerce_df_columns_to_numeric(df, column_list):
    df[column_list] = df[column_list].apply(pd.to_numeric, errors='coerce')

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['col1','col2','col3'])

coerce_df_columns_to_numeric(df, ['col2','col3'])

View more solutions

Comments

[hernamesbarbara]

Yes! pd.DataFrame has a dtype argument that might let you do w/ you're looking for. df = pd.DataFrame(a, columns=['one', 'two', 'three'], dtype=float) In [2]: df.dtypes Out[2]: one object two float64 three float64 dtype: object

[orange]

When I try as suggested, I get a warning SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead. This may have been introduced in a newer version of pandas and I don't see anything wrong as a result, but I just wonder what this warning is all about. Any idea?

[A.Wan]

@orange the warning is to alert users to potentially confusing behavior with chained operations, and with pandas returning copies of rather than editing dataframes. see stackoverflow.com/questions/20625582/… and related.

[Vitaly Isaev]

That's a good method, but it doesn't work when there are NaN in a column. Have no idea why NaN just cannot stay NaN when casting float to int: ValueError: Cannot convert NA to integer

[Rob]

Also, unlike .astype(float), this will convert strings to NaNs instead of raising an error

[Pietro Battiston]

@VitalyIsaev: because numpy ints don't have a representation for NaN: pandas.pydata.org/pandas-docs/stable/…

[Matti Lyra]

.convert_objects is depracated since 0.17 - use df.to_numeric instead

[Gill Bates]

@hernamesbarbara, is it possible to pass different dtype's for different columns?

[FichteFoll]

@GillBates yes, in a dictionary. df = pd.DataFrame(a, columns=['one', 'two', 'three'], dtype={'one': str, 'two': int, 'three': float}). I'm having a hard time finding the specification for accepted "dtype" values though. A list would be nice (currently I do dict(enumerate(my_list))).

[wyx]

@FichteFoll It can't work .It raise ValueError: entry not a 2- or 3- tuple

[H. Vabri]

Mind you that when applying this on a column containing the strings ``` 'True' ``` and ``` 'False' ``` using the data_type bool, everything is changed to True.

[Soren]

This also converts datetimes.

[Naypa]

This option you can also convert to type "category"

[jvalenti]

what if you wanted to use column indexes instead of column names?

[fogx]

is there a way to error=coerce in astype()?

[BSalita]

I like how df.info() provides memory usage in the final line.

[Trenton McKinney]

This duplicate has been flagged to a moderator, as per Flag Duplicate Answers on the same Question. While this is an answer, it duplicates code in the accepted answer and other answers. There is no additional value for SO to keep many answers with the same solution and there doesn't need to be an example for every type. Instead, upvote existing answers.

[Kishore]

@fogx No there is not. You can refer here