有没有办法只复制熊猫数据框架的结构(而不是数据) ？

我从某处接收到一个 DataFrame，并希望创建另一个具有相同数量和名称的列和行(索引)的 DataFrame。例如，假设原始数据框架创建为

import pandas as pd
df1 = pd.DataFrame([[11,12],[21,22]], columns=['c1','c2'], index=['i1','i2'])

我通过显式定义列和名称复制了这个结构:

df2 = pd.DataFrame(columns=df1.columns, index=df1.index)

我不想复制数据，否则我可以只写 df2 = df1.copy()。换句话说，在创建 df2之后，它必须只包含 NaN 元素:

In [1]: df1
Out[1]:
c1  c2
i1  11  12
i2  21  22


In [2]: df2
Out[2]:
c1   c2
i1  NaN  NaN
i2  NaN  NaN

还有比这更惯用的方法吗？

71248 次浏览
小开

In version 0.18 of pandas, the DataFrame constructor has no options for creating a dataframe like another dataframe with NaN instead of the values.

The code you use df2 = pd.DataFrame(columns=df1.columns, index=df1.index) is the most logical way, the only way to improve on it is to spell out even more what you are doing is to add data=None, so that other coders directly see that you intentionally leave out the data from this new DataFrame you are creating.

TLDR: So my suggestion is:

Explicit is better than implicit

df2 = pd.DataFrame(data=None, columns=df1.columns, index=df1.index)

Very much like yours, but more spelled out.

小开

Let's start with some sample data

In [1]: import pandas as pd


In [2]: df = pd.DataFrame([[1, 'a'], [2, 'b'], [3, 'c']],
...:                   columns=['num', 'char'])


In [3]: df
Out[3]:
num char
0    1    a
1    2    b
2    3    c


In [4]: df.dtypes
Out[4]:
num      int64
char    object
dtype: object

Now let's use a simple DataFrame initialization using the columns of the original DataFrame but providing no data:

In [5]: empty_copy_1 = pd.DataFrame(data=None, columns=df.columns)


In [6]: empty_copy_1
Out[6]:
Empty DataFrame
Columns: [num, char]
Index: []


In [7]: empty_copy_1.dtypes
Out[7]:
num     object
char    object
dtype: object

As you can see, the column data types are not the same as in our original DataFrame.

So, if you want to preserve the column dtype...

If you want to preserve the column data types you need to construct the DataFrame one Series at a time 

In [8]: empty_copy_2 = pd.DataFrame.from_items([
...:     (name, pd.Series(data=None, dtype=series.dtype))
...:     for name, series in df.iteritems()])


In [9]: empty_copy_2
Out[9]:
Empty DataFrame
Columns: [num, char]
Index: []


In [10]: empty_copy_2.dtypes
Out[10]:
num      int64
char    object
dtype: object
小开

A simple alternative -- first copy the basic structure or indexes and columns with datatype from the original dataframe (df1) into df2

df2 = df1.iloc[0:0]

Then fill your dataframe with empty rows -- pseudocode that will need to be adapted to better match your actual structure:

s = pd.Series([Nan,Nan,Nan], index=['Col1', 'Col2', 'Col3'])

loop through the rows in df1

df2 = df2.append(s)
小开
最佳答案

That's a job for reindex_like. Start with the original:

df1 = pd.DataFrame([[11, 12], [21, 22]], columns=['c1', 'c2'], index=['i1', 'i2'])

Construct an empty DataFrame and reindex it like df1:

pd.DataFrame().reindex_like(df1)
Out:
c1  c2
i1 NaN NaN
i2 NaN NaN
小开

You can simply mask by notna() i.e 

df1 = pd.DataFrame([[11, 12], [21, 22]], columns=['c1', 'c2'], index=['i1', 'i2'])


df2 = df1.mask(df1.notna())


c1  c2
i1 NaN NaN
i2 NaN NaN
小开

This has worked for me in pandas 0.22: df2 = pd.DataFrame(index=df.index.delete(slice(None)), columns=df.columns)

Convert types: df2 = df2.astype(df.dtypes)

delete(slice(None)) In case you do not want to keep the values ​​of the indexes.

小开

I know this is an old question, but I thought I would add my two cents.

def df_cols_like(df):
"""
Returns an empty data frame with the same column names and types as df
"""
df2 = pd.DataFrame({i[0]: pd.Series(dtype=i[1])
for i in df.dtypes.iteritems()},
columns=df.dtypes.index)
return df2

This approach centers around the df.dtypes attribute of the input data frame, df, which is a pd.Series. A pd.DataFrame is constructed from a dictionary of empty pd.Series objects named using the input column names with the column order being taken from the input df.

小开

Not exactly answering this question, but a similar one for people coming here via a search engine

My case was creating a copy of the data frame without data and without index. One can achieve this by doing the following. This will maintain the dtypes of the columns.

empty_copy = df.drop(df.index)
小开

A simple way to copy df structure into df2 is:

df2 = pd.DataFrame(columns=df.columns)
小开

To preserve column type you can use the astype method, like pd.DataFrame(columns=df1.columns).astype(df1.dtypes)

import pandas as pd


df1 = pd.DataFrame(
[
[11, 12, 'Alice'],
[21, 22, 'Bob']
],
columns=['c1', 'c2', 'c3'],
index=['i1', 'i2']
)


df2 = pd.DataFrame(columns=df1.columns).astype(df1.dtypes)
print(df2.shape)
print(df2.dtypes)

output:

(0, 3)
c1     int64
c2     int64
c3    object
dtype: object

Working example


提交答案