更改Pandas中列的数据类型

问题:更改Pandas中列的数据类型

我想将表示为列表列表的表转换为Pandas DataFrame。作为一个极其简化的示例:

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

将列转换为适当类型的最佳方法是什么,在这种情况下,将列2和3转换为浮点数?有没有一种方法可以在转换为DataFrame时指定类型?还是先创建DataFrame然后遍历各列以更改各列的类型更好?理想情况下,我想以动态方式执行此操作,因为可以有数百个列,并且我不想确切指定哪些列属于哪种类型。我可以保证的是,每一列都包含相同类型的值。

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.


回答 0

您可以使用三种主要选项来转换熊猫的类型:

  1. to_numeric()提供安全地将非数字类型(例如字符串)转换为合适的数字类型的功能。(另请参见to_datetime()to_timedelta()。)

  2. astype()-将(几乎)任何类型转换为(几乎)任何其他类型(即使这样做不一定明智)。还允许您转换为分类类型(非常有用)。

  3. infer_objects() -一种实用方法,如果可能的话,将保存Python对象的对象列转换为熊猫类型。

继续阅读以获取每种方法的更详细的解释和用法。


1。 to_numeric()

将DataFrame的一列或多列转换为数值的最佳方法是使用pandas.to_numeric()

此函数将尝试将非数字对象(例如字符串)适当地更改为整数或浮点数。

基本用法

输入to_numeric()是DataFrame的Series或单个列。

>>> 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

如您所见,将返回一个新的Series。请记住,将此输出分配给变量或列名以继续使用它:

# convert Series
my_series = pd.to_numeric(my_series)

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

您还可以通过以下apply()方法使用它来转换DataFrame的多个列:

# 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)

只要您的值都可以转换,那可能就是您所需要的。

错误处理

但是,如果某些值不能转换为数字类型怎么办?

to_numeric()还使用errors关键字参数,该参数允许您将非数字值强制为NaN,或仅忽略包含这些值的列。

这是使用一系列s具有对象dtype 的字符串的示例:

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

如果无法转换值,则默认行为是引发。在这种情况下,它不能处理字符串“ pandas”:

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

我们可能希望将“ pandas”视为丢失/错误的数值,而不是失败。我们可以NaN使用errors关键字参数将无效值强制如下:

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

第三个选项errors只是在遇到无效值时忽略该操作:

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

当您要转换整个DataFrame,但又不知道我们哪些列可以可靠地转换为数字类型时,最后一个选项特别有用。在这种情况下,只需写:

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

该函数将应用于DataFrame的每一列。可以转换为数字类型的列将被转换,而不能转换(例如,它们包含非数字字符串或日期)的列将被保留。

下垂

默认情况下,with转换to_numeric()将为您提供a int64float64dtype(或平台固有的任何整数宽度)。

通常这就是您想要的,但是如果您想节省一些内存并使用更紧凑的dtype,如float32int8呢?

to_numeric()您可以选择向下转换为“整数”,“有符号”,“无符号”,“浮点型”。这是一个简单s的整数类型系列的示例:

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

向下转换为“整数”将使用可以保存值的最小整数:

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

向下转换为“ float”类似地选择了一个比普通浮点型小的类型:

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

2。 astype()

astype()方法使您可以明确表示希望DataFrame或Series具有的dtype。它非常通用,可以尝试从一种类型转换为另一种类型。

基本用法

只需选择一个类型:您可以使用NumPy dtype(例如np.int16),某些Python类型(例如bool)或特定于熊猫的类型(例如类别dtype)。

在要转换的对象上调用方法,然后astype()将尝试为您转换:

# 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')

注意,我说的是“尝试”-如果astype()不知道如何在Series或DataFrame中转换值,则会引发错误。例如,如果您具有NaNor inf值,则尝试将其转换为整数时会出错。

从熊猫0.20.0开始,可以通过传递来抑制此错误errors='ignore'。您的原始对象将保持原样返回。

小心

astype()功能强大,但有时会“错误地”转换值。例如:

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

这些都是小整数,那么如何转换为无符号8位类型以节省内存呢?

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

转换工作,但-7包裹轮成为249(如2 8 – 7)!

尝试使用向下转换来pd.to_numeric(s, downcast='unsigned')帮助防止此错误。


3。 infer_objects()

pandas的0.21.0版引入了infer_objects()将具有对象数据类型的DataFrame列转换为更特定类型(软转换)的方法。

例如,这是一个带有两列对象类型的DataFrame。一个保存实际的整数,另一个保存代表整数的字符串:

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

使用infer_objects(),您可以将列’a’的类型更改为int64:

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

由于列“ b”的值是字符串而不是整数,因此已被保留。如果要尝试强制将两列都转换为整数类型,则可以df.astype(int)改用。

You have three 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.

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 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.


回答 1

这个怎么样?

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

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

回答 2

下面的代码将更改列的数据类型。

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

您可以给数据类型代替数据类型。您想要什么,例如str,float,int等。

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.


回答 3

当我只需要指定特定的列并且想要明确时,我就使用了(每个DOCS LOCATION):

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

因此,使用原始问题,但为其提供列名称…

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'})

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'})

回答 4

这是一个函数,该函数将DataFrame和列列表作为参数,并将列中的所有数据强制转换为数字。

# 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')

因此,以您的示例为例:

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'])

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'])

回答 5

如何创建两个数据框,每个数据框的列具有不同的数据类型,然后将它们附加在一起?

d1 = pd.DataFrame(columns=[ 'float_column' ], dtype=float)
d1 = d1.append(pd.DataFrame(columns=[ 'string_column' ], dtype=str))

结果

In[8}:  d1.dtypes
Out[8]: 
float_column     float64
string_column     object
dtype: object

创建数据框后,可以在第一列中填充浮点变量,并在第二列中填充字符串(或所需的任何数据类型)。

How about creating two dataframes, each with different data types for their columns, and then appending them together?

d1 = pd.DataFrame(columns=[ 'float_column' ], dtype=float)
d1 = d1.append(pd.DataFrame(columns=[ 'string_column' ], dtype=str))

Results

In[8}:  d1.dtypes
Out[8]: 
float_column     float64
string_column     object
dtype: object

After the dataframe is created, you can populate it with floating point variables in the 1st column, and strings (or any data type you desire) in the 2nd column.


回答 6

熊猫> = 1.0

这是一张图表,总结了熊猫中一些最重要的转换。

转换为字符串很简单.astype(str),未在图中显示。

“硬”对“软”转换

注意,在这种情况下,“转换”既可以指将文本数据转换为实际数据类型(硬转换),也可以为对象列中的数据推断更合适的数据类型(软转换)。为了说明不同之处,请看一下

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                                                     

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                                                     

回答 7

我以为我遇到了同样的问题,但实际上我有一些细微的差别,使问题更容易解决。对于其他正在看这个问题的人,值得检查输入列表的格式。就我而言,数字最初是浮动的,而不是问题中的字符串:

a = [['a', 1.2, 4.2], ['b', 70, 0.03], ['x', 5, 0]]

但是通过在创建数据框之前过多处理列表,我丢失了类型,所有内容都变成了字符串。

通过numpy数组创建数据框

df = pd.DataFrame(np.array(a))

df
Out[5]: 
   0    1     2
0  a  1.2   4.2
1  b   70  0.03
2  x    5     0

df[1].dtype
Out[7]: dtype('O')

给出与问题相同的数据帧,其中第1列和第2列中的条目被视为字符串。但是做

df = pd.DataFrame(a)

df
Out[10]: 
   0     1     2
0  a   1.2  4.20
1  b  70.0  0.03
2  x   5.0  0.00

df[1].dtype
Out[11]: dtype('float64')

确实给出了具有正确格式的列的数据框

I thought I had the same problem but actually I have a slight difference that makes the problem easier to solve. For others looking at this question it’s worth checking the format of your input list. In my case the numbers are initially floats not strings as in the question:

a = [['a', 1.2, 4.2], ['b', 70, 0.03], ['x', 5, 0]]

but by processing the list too much before creating the dataframe I lose the types and everything becomes a string.

Creating the data frame via a numpy array

df = pd.DataFrame(np.array(a))

df
Out[5]: 
   0    1     2
0  a  1.2   4.2
1  b   70  0.03
2  x    5     0

df[1].dtype
Out[7]: dtype('O')

gives the same data frame as in the question, where the entries in columns 1 and 2 are considered as strings. However doing

df = pd.DataFrame(a)

df
Out[10]: 
   0     1     2
0  a   1.2  4.20
1  b  70.0  0.03
2  x   5.0  0.00

df[1].dtype
Out[11]: dtype('float64')

does actually give a data frame with the columns in the correct format


回答 8

从熊猫1.0.0开始,我们有了pandas.DataFrame.convert_dtypes。您甚至可以控制要转换的类型!

In [40]: df = pd.DataFrame(
    ...:     {
    ...:         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
    ...:         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
    ...:         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
    ...:         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
    ...:         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
    ...:         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
    ...:     }
    ...: )

In [41]: dff = df.copy()

In [42]: df 
Out[42]: 
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

In [43]: df.dtypes
Out[43]: 
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

In [44]: df = df.convert_dtypes()

In [45]: df.dtypes
Out[45]: 
a      Int32
b     string
c    boolean
d     string
e      Int64
f    float64
dtype: object

In [46]: dff = dff.convert_dtypes(convert_boolean = False)

In [47]: dff.dtypes
Out[47]: 
a      Int32
b     string
c     object
d     string
e      Int64
f    float64
dtype: object

Starting pandas 1.0.0, we have pandas.DataFrame.convert_dtypes. You can even control what types to convert!

In [40]: df = pd.DataFrame(
    ...:     {
    ...:         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
    ...:         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
    ...:         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
    ...:         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
    ...:         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
    ...:         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
    ...:     }
    ...: )

In [41]: dff = df.copy()

In [42]: df 
Out[42]: 
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

In [43]: df.dtypes
Out[43]: 
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

In [44]: df = df.convert_dtypes()

In [45]: df.dtypes
Out[45]: 
a      Int32
b     string
c    boolean
d     string
e      Int64
f    float64
dtype: object

In [46]: dff = dff.convert_dtypes(convert_boolean = False)

In [47]: dff.dtypes
Out[47]: 
a      Int32
b     string
c     object
d     string
e      Int64
f    float64
dtype: object