Python 在 Pandas 中创建空数据框指定列类型
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/36462257/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Create Empty Dataframe in Pandas specifying column types
提问by Vincent
I'm trying to create an empty data frame with an index and specify the column types. The way I am doing it is the following:
我正在尝试创建一个带有索引的空数据框并指定列类型。我这样做的方式如下：
df = pd.DataFrame(index=['pbp'],columns=['contract',
'state_and_county_code',
'state',
'county',
'starting_membership',
'starting_raw_raf',
'enrollment_trend',
'projected_membership',
'projected_raf'],
dtype=['str', 'str', 'str', 'str', 'int', 'float', 'float', 'int', 'float'])
However, I get the following error,
但是，我收到以下错误，
TypeError: data type not understood
What does this mean?
这是什么意思？
回答by ryanjdillon
You can do it like this
你可以这样做
import numpy
import pandas
dtypes = numpy.dtype([
('a', str),
('b', int),
('c', float),
('d', numpy.datetime64),
])
data = numpy.empty(0, dtype=dtypes)
df = pandas.DataFrame(data)
回答by user48956
This really smells like a bug.
这真的闻起来像一个错误。
Here's another (simpler) solution.
这是另一个（更简单的）解决方案。
import pandas as pd
import numpy as np
def df_empty(columns, dtypes, index=None):
assert len(columns)==len(dtypes)
df = pd.DataFrame(index=index)
for c,d in zip(columns, dtypes):
df[c] = pd.Series(dtype=d)
return df
df = df_empty(['a', 'b'], dtypes=[np.int64, np.int64])
print(list(df.dtypes)) # int64, int64
回答by Alberto
You can use the following:
您可以使用以下内容：
df = pd.DataFrame({'a': pd.Series([], dtype='int'),
'b': pd.Series([], dtype='str'),
'c': pd.Series([], dtype='float')})
then if you call df you have
那么如果你打电话给 df 你有
>>> df
Empty DataFrame
Columns: [a, b, c]
Index: []
and if you check its types
如果你检查它的类型
>>> df.dtypes
a int32
b object
c float64
dtype: object
回答by SummerEla
This is an old question, but I don't see a solid answer (although @eric_g was super close).
这是一个老问题，但我没有看到可靠的答案（尽管 @eric_g 非常接近）。
You just need to create an empty dataframe with a list of dictionary key:value pairs. The key being your column name, and the value being an empty data type.
您只需要创建一个包含字典键值对列表的空数据框。键是您的列名，值是空数据类型。
So in your example dataset, it would look as follows:
因此，在您的示例数据集中，它将如下所示：
df = pd.DataFrame(,columns=[{'contract':'',
'state_and_county_code':'',
'state':'',
'county':'',
'starting_membership':int(),
'starting_raw_raf':float(),
'enrollment_trend':float(),
'projected_membership':int(),
'projected_raf':float(),
'pbp':int() #just guessing on this data type
}]).set_index=("pbp")
Alternatively, this works in pandas .25 and python 3.7:
或者，这适用于 pandas .25 和 python 3.7：
df = pd.DataFrame({'contract':'',
'state_and_county_code':'',
'state':'',
'county':'',
'starting_membership':int(),
'starting_raw_raf':float(),
'enrollment_trend':float(),
'projected_membership':int(),
'projected_raf':float(),
'pbp':int() #just guessing on this data type
},
index=[1])
回答by ptrj
Just a remark.
只是一个评论。
You can get around the Type Error using np.dtype:
您可以使用np.dtype以下方法解决类型错误：
pd.DataFrame(index = ['pbp'], columns = ['a','b'], dtype = np.dtype([('str','float')]))
but you get instead:
但你得到：
NotImplementedError: compound dtypes are not implementedin the DataFrame constructor
回答by JaminSore
I found this question after running into the same issue. I prefer the following solution (Python 3) for creating an empty DataFrame with no index.
遇到同样的问题后，我发现了这个问题。我更喜欢以下解决方案（Python 3）来创建一个没有 index的空 DataFrame 。
import numpy as np
import pandas as pd
def make_empty_typed_df(dtype):
tdict = np.typeDict
types = tuple(tdict.get(t, t) for (_, t, *__) in dtype)
if any(t == np.void for t in types):
raise NotImplementedError('Not Implemented for columns of type "void"')
return pd.DataFrame.from_records(np.array([tuple(t() for t in types)], dtype=dtype)).iloc[:0, :]
Testing this out ...
测试一下...
from itertools import chain
dtype = [('col%d' % i, t) for i, t in enumerate(chain(np.typeDict, set(np.typeDict.values())))]
dtype = [(c, t) for (c, t) in dtype if (np.typeDict.get(t, t) != np.void) and not isinstance(t, int)]
print(make_empty_typed_df(dtype))
Out:
出去：
Empty DataFrame
Columns: [col0, col6, col16, col23, col24, col25, col26, col27, col29, col30, col31, col32, col33, col34, col35, col36, col37, col38, col39, col40, col41, col42, col43, col44, col45, col46, col47, col48, col49, col50, col51, col52, col53, col54, col55, col56, col57, col58, col60, col61, col62, col63, col64, col65, col66, col67, col68, col69, col70, col71, col72, col73, col74, col75, col76, col77, col78, col79, col80, col81, col82, col83, col84, col85, col86, col87, col88, col89, col90, col91, col92, col93, col95, col96, col97, col98, col99, col100, col101, col102, col103, col104, col105, col106, col107, col108, col109, col110, col111, col112, col113, col114, col115, col117, col119, col120, col121, col122, col123, col124, ...]
Index: []
[0 rows x 146 columns]
And the datatypes ...
和数据类型...
print(make_empty_typed_df(dtype).dtypes)
Out:
出去：
col0 timedelta64[ns]
col6 uint16
col16 uint64
col23 int8
col24 timedelta64[ns]
col25 bool
col26 complex64
col27 int64
col29 float64
col30 int8
col31 float16
col32 uint64
col33 uint8
col34 object
col35 complex128
col36 int64
col37 int16
col38 int32
col39 int32
col40 float16
col41 object
col42 uint64
col43 object
col44 int16
col45 object
col46 int64
col47 int16
col48 uint32
col49 object
col50 uint64
...
col144 int32
col145 bool
col146 float64
col147 datetime64[ns]
col148 object
col149 object
col150 complex128
col151 timedelta64[ns]
col152 int32
col153 uint8
col154 float64
col156 int64
col157 uint32
col158 object
col159 int8
col160 int32
col161 uint64
col162 int16
col163 uint32
col164 object
col165 datetime64[ns]
col166 float32
col167 bool
col168 float64
col169 complex128
col170 float16
col171 object
col172 uint16
col173 complex64
col174 complex128
dtype: object
Adding an index gets tricky because there isn't a true missing value for most data types so they end up getting cast to some other type with a native missing value (e.g., ints are cast to floats or objects), but if you have complete data of the types you've specified, then you can always insert rows as needed, and your types will be respected. This can be accomplished with:
添加索引变得棘手，因为大多数数据类型都没有真正的缺失值，因此它们最终会被强制转换为具有本机缺失值的其他类型（例如，ints 被强制转换为floats 或objects），但如果您有完整的您指定的类型的数据，然后您可以随时根据需要插入行，并且您的类型将得到尊重。这可以通过以下方式完成：
df.loc[index, :] = new_row
Again, as @Hun pointed out, this NOT how Pandas is intended to be used.
同样，正如@Hun 指出的那样，这不是 Pandas 的用途。
回答by Eric G.
You can do this by passing a dictionary into the DataFrame constructor:
您可以通过将字典传递给 DataFrame 构造函数来完成此操作：
df = pd.DataFrame(index=['pbp'],
data={'contract' : np.full(1, "", dtype=str),
'starting_membership' : np.full(1, np.nan, dtype=float),
'projected_membership' : np.full(1, np.nan, dtype=int)
}
)
This will correctlygive you a dataframe that looks like:
这将正确地为您提供一个如下所示的数据框：
contract projected_membership starting_membership
pbp "" NaN -9223372036854775808
With dtypes:
使用数据类型：
contract object
projected_membership float64
starting_membership int64
That said, there are two things to note:
也就是说，有两点需要注意：
1) strisn't actually a type that a DataFrame column can handle; instead it falls back to the general case object. It'll still work properly.
1)str实际上不是 DataFrame 列可以处理的类型；相反，它退回到一般情况object。它仍然会正常工作。
2) Why don't you see NaNunder starting_membership? Well, NaNis only defined for floats; there is no "None" value for integers, so it casts np.NaNto an integer. If you want a different default value, you can change that in the np.fullcall.
2）你为什么看不到NaN下面starting_membership？好吧，NaN只为浮点数定义；整数没有“无”值，因此它转换np.NaN为整数。如果您想要不同的默认值，您可以在np.full调用中更改它。
回答by Hun
pandas doesn't offer pure integer column. You can either use float column and convert that column to integer as needed or treat it like an object. What you are trying to implement is not the way pandas is supposed to be used. But if you REALLY REALLY want that, you can get around the TypeError message by doing this.
pandas 不提供纯整数列。您可以使用浮点列并根据需要将该列转换为整数，也可以将其视为对象。您尝试实现的不是应该使用熊猫的方式。但是如果你真的真的想要那个，你可以通过这样做来绕过 TypeError 消息。
df1 = pd.DataFrame(index=['pbp'], columns=['str1','str2','str2'], dtype=str)
df2 = pd.DataFrame(index=['pbp'], columns=['int1','int2'], dtype=int)
df3 = pd.DataFrame(index=['pbp'], columns=['flt1','flt2'], dtype=float)
df = pd.concat([df1, df2, df3], axis=1)
str1 str2 str2 int1 int2 flt1 flt2
pbp NaN NaN NaN NaN NaN NaN NaN
You can rearrange the col order as you like. But again, this is not the way pandas was supposed to be used.
您可以根据需要重新排列颜色顺序。但同样，这不是应该使用熊猫的方式。
df.dtypes
str1 object
str2 object
str2 object
int1 object
int2 object
flt1 float64
flt2 float64
dtype: object
Note that int is treated as object.
请注意， int 被视为对象。
回答by jdehesa
I found the easiest workaround for me was to simply concatenate a list of empty series for each individual column:
我发现对我来说最简单的解决方法是简单地为每个单独的列连接一个空系列列表：
import pandas as pd
columns = ['contract',
'state_and_county_code',
'state',
'county',
'starting_membership',
'starting_raw_raf',
'enrollment_trend',
'projected_membership',
'projected_raf']
dtype = ['str', 'str', 'str', 'str', 'int', 'float', 'float', 'int', 'float']
df = pd.concat([pd.Series(name=col, dtype=dt) for col, dt in zip(columns, dtype)], axis=1)
df.info()
# <class 'pandas.core.frame.DataFrame'>
# Index: 0 entries
# Data columns (total 9 columns):
# contract 0 non-null object
# state_and_county_code 0 non-null object
# state 0 non-null object
# county 0 non-null object
# starting_membership 0 non-null int32
# starting_raw_raf 0 non-null float64
# enrollment_trend 0 non-null float64
# projected_membership 0 non-null int32
# projected_raf 0 non-null float64
# dtypes: float64(3), int32(2), object(4)
# memory usage: 0.0+ bytes
回答by Korhan
My solution (without setting an index) is to initialize a dataframe with column names and specify data types using astype()method.
我的解决方案（不设置索引）是使用列名初始化数据框并使用astype()方法指定数据类型。
df = pd.DataFrame(columns=['contract',
'state_and_county_code',
'state',
'county',
'starting_membership',
'starting_raw_raf',
'enrollment_trend',
'projected_membership',
'projected_raf'])
df = df.astype( dtype={'contract' : str,
'state_and_county_code': str,
'state': str,
'county': str,
'starting_membership': int,
'starting_raw_raf': float,
'enrollment_trend': float,
'projected_membership': int,
'projected_raf': float})
