Python 在指定列类型的数据框中创建空数据框
我试图创建一个带有索引的空数据框，并指定列类型。我的做法如下：Python 在指定列类型的数据框中创建空数据框,python,pandas,Python,Pandas,我试图创建一个带有索引的空数据框，并指定列类型。我的做法如下： df = pd.DataFrame(index=['pbp'],columns=['contract', 'state_and_county_code', 'state', 'county'
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'])
df = pd.DataFrame({'a': pd.Series([], dtype='int'),
'b': pd.Series([], dtype='str'),
'c': pd.Series([], dtype='float')})
cdt={i[0]: i[1] for i in zip(columns, dtype)} # make column type dict
pdf=pd.DataFrame(columns=list(cdt)) # create empty dataframe
pdf=pdf.astype(cdt) # set desired column types
TypeError: data type not understood
这意味着什么？熊猫不提供纯整数列。您可以使用float列并根据需要将该列转换为整数，也可以将其视为对象。你试图实现的不是熊猫应该被使用的方式。但是，如果您真的想要这样做，您可以通过这样做绕过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
df.dtypes
str1 object
str2 object
str2 object
int1 object
int2 object
flt1 float64
flt2 float64
dtype: object
np.dtypepd.DataFrame(index = ['pbp'], columns = ['a','b'], dtype = np.dtype([('str','float')]))
NotImplementedError: compound dtypes are not implementedin the DataFrame constructor
我在遇到同样的问题后发现了这个问题。我更喜欢下面的解决方案（Python3）来创建没有索引的空数据帧 测试这个
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))
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]
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
print(make_empty_typed_df(dtype).dtypes)
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]
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
intfloatobjectdf.loc[index, :] = new_row
同样，正如@Hun所指出的，这不是熊猫的使用方式。您可以通过将字典传递到数据帧构造函数来实现这一点：
df.dtypes
str1 object
str2 object
str2 object
int1 object
int2 object
flt1 float64
flt2 float64
dtype: object
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)
}
)
contract projected_membership starting_membership
pbp "" NaN -9223372036854775808
contract object
projected_membership float64
starting_membership int64
str对象2） 您为什么不在
开始加入NaNNaNnp.NaNnp.fullimport 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
这是一个老问题，但我看不到可靠的答案（尽管@eric_g非常接近） 您只需要创建一个带有键：值对字典的空数据帧。键是列名，值是空数据类型 因此，在示例数据集中，它如下所示（pandas 0.25和python 3.7）： 在旧版本中，可能需要执行以下操作：
df = pd.DataFrame(columns=[variables])
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)
我发现对我来说，最简单的解决方法是为每个列连接一个空序列列表：
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
将熊猫作为pd导入
列=[“合同”，
“州和县代码”，
“国家”，
"县",，
"入会",，
“启动raf”，
“入学趋势”，
"预计的大学会籍",，
“预计的皇家空军”]
数据类型=['str'，'str'，'str'，'str'，'int'，'float'，'float'，'int'，'float']
df=pd.concat（[pd.Series（name=col，dtype=dt）表示col，dt在zip中（columns，dtype）]，axis=1）
df.info（）
#
#索引：0个条目
#数据列（共9列）：
#协定0非空对象
#州和县代码0非空对象
#状态0为非空对象
#县0非空对象
#正在启动\u成员身份0非空int32
#正在启动\u原始\u raf 0非空浮点64
#注册趋势0非空浮点64
#计划的\u成员身份0非空int32
#投影的_raf 0非空浮点64
#数据类型：float64（3）、int32（2）、object（4）
#内存使用：0.0+字节
astype（）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})
您可以使用以下选项：
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'])
df = pd.DataFrame({'a': pd.Series([], dtype='int'),
'b': pd.Series([], dtype='str'),
'c': pd.Series([], dtype='float')})
cdt={i[0]: i[1] for i in zip(columns, dtype)} # make column type dict
pdf=pd.DataFrame(columns=list(cdt)) # create empty dataframe
pdf=pdf.astype(cdt) # set desired column types
>>> df
Empty DataFrame
Columns: [a, b, c]
Index: []
>>> df.dtypes
a int32
b object
c float64
dtype: object
在指定列类型的数据框中创建空数据框 我认为这是完美的
import pandas as pd
c1 = pd.Series(data=None, dtype='string', name='c1')
c2 = pd.Series(data=None, dtype='bool', name='c2')
c3 = pd.Series(data=None, dtype='float', name='c3')
c4 = pd.Series(data=None, dtype='int', name='c4')
df = pd.concat([c1, c2, c3, c4], axis=1)
df.info('verbose')
<class 'pandas.core.frame.DataFrame'>
Index: 0 entries
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 c1 0 non-null string
1 c2 0 non-null bool
2 c3 0 non-null float64
3 c4 0 non-null int32
dtypes: bool(1), float64(1), int32(1), string(1)
memory usage: 0.0+ bytes
索引：0个条目
数据列（共4列）：
#列非空计数数据类型
--- ------ -------------- -----
0 c1 0非空字符串
1 c2 0非空布尔值
2 c3 0非空浮点64
3 c4 0非空int32
数据类型：bool（1）、float64（1）、int32（1）、string（1）
内存使用：0.0+字节
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'])
df = pd.DataFrame({'a': pd.Series([], dtype='int'),
'b': pd.Series([], dtype='str'),
'c': pd.Series([], dtype='float')})
cdt={i[0]: i[1] for i in zip(columns, dtype)} # make column type dict
pdf=pd.DataFrame(columns=list(cdt)) # create empty dataframe
pdf=pdf.astype(cdt) # set desired column types
DataFrame doc说构造函数调用中只允许一个数据类型。我认为您不能用这种方式指定数据类型，您可以传递一个类型，例如
strdtype=intintnan