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We will look at methods for changing the data type of columns in a Pandas Dataframe, as well as options like to_numaric, , as_typeand infer_objects. We will also discuss how to to_numaricuse downcastingthe option in .

to_numericMethod to convert columns to numeric values ​​in Pandas

to_numeric()is the best way to convert one or more columns of a dataFrame to numeric values. It also attempts to change non-numeric objects (such as strings) to integers or floating point numbers as appropriate. to_numeric()The input can be a column of Seriesor DataFrame. If some values ​​cannot be converted to a numeric type, to_numeric()allows us to coerce non-numeric values ​​to NaN.

Code example:

# python 3.x
import pandas as pd

s = pd.Series(["12", "12", "4.7", "asad", "3.0"])
print(s)
print("------------------------------")
print(pd.to_numeric(s, errors="coerce"))

Output:

0      12
1      12
2     4.7
3    asad
4     3.0
dtype: object0    12.0
1    12.0
2     4.7
3     NaN
4     3.0
dtype: float64

By default, to_numeric()we are given int64a float64dtype of . We can use an option to convert to integer, signed, unsignedor float:

# python 3.x
import pandas as pd

s = pd.Series([-3, 1, -5])
print(s)
print(pd.to_numeric(s, downcast="integer"))

Output:

0   -3
1    1
2   -5
dtype: int64
0   -3
1    1
2   -5
dtype: int8

astype()Method to convert one type to any other data type

astype() method allows us to be explicit about the dtype we want to convert. By astype()passing the argument inside the method, we can go from one data type to another.

Consider the following code:

# python 3.x
import pandas as pd

c = [["x", "1.23", "14.2"], ["y", "20", "0.11"], ["z", "3", "10"]]
df = pd.DataFrame(c, columns=["first", "second", "third"])
print(df)
df[["second", "third"]] = df[["second", "third"]].astype(float)
print("Converting..................")
print("............................")
print(df)

Output:

  first second third
0     x   1.23  14.2
1     y     20  0.11
2     z      3    10
Converting..................
............................
  first  second  third
0     x    1.23  14.20
1     y   20.00   0.11
2     z    3.00  10.00

infer_objects()Method to convert column data type to a more specific type

infer_objects()The method introduced in Pandas version 0.21.0 is used to dataFrameconvert a column of to a more specific data type (soft conversion).

Consider the following code:

# python 3.x
import pandas as pd

df = pd.DataFrame({"a": [3, 12, 5], "b": [3.0, 2.6, 1.1]}, dtype="object")
print(df.dtypes)
df = df.infer_objects()
print("Infering..................")
print("............................")
print(df.dtypes)

Output:

a    object
b    object
dtype: object
Infering..................
............................
a      int64
b    float64
dtype: object