[英]Create pd.DataFrame from dictionary with multi-dimensional array
I've the following dictionary:我有以下字典:
dictA = {'A': [[1, 2, 3], [1, 2, 3], [1, 2, 3]],
'B': [[4, 4, 4], [4, 4, 4],],
'C': [[4, 6, 0]]
}
I want to convert it to a pd.DataFrame()
, expecting this:我想将它转换为pd.DataFrame()
,期待这个:
id ColA ColB ColC
0 1 4 4
1 2 4 6
2 3 4 0
3 1 4
4 2 4
5 3 4
6 1
7 2
8 3
How can I do that?我怎样才能做到这一点? I'm trying我想
pd.DataFrame(dictAll.items(), columns=['ColA', 'ColB', 'ColC'])
But it obviously doesn't work!但这显然行不通!
Here is how:方法如下:
import pandas as pd
import numpy as np
dictA = {'A': [[1, 2, 3], [1, 2, 3], [1, 2, 3]],
'B': [[4, 4, 4], [4, 4, 4],],
'C': [[4, 6, 0]]}
df = pd.DataFrame(dict([(f'Col{k}', pd.Series([a for b in v for a in b])) for k,v in dictA.items()])).replace(np.nan, '')
print(df)
Output:输出:
ColA ColB ColC
0 1 4 4
1 2 4 6
2 3 4 0
3 1 4
4 2 4
5 3 4
6 1
7 2
8 3
Now, let's have a look at the problem one step at a time.现在,让我们一步一步地看一下这个问题。
The first thing we might try is simply:我们可以尝试的第一件事很简单:
df = pd.DataFrame(dictA) print(df)
Which, of course, return this error:当然,这会返回此错误:
ValueError: arrays must all be same length
So now we need a way to be able to create dataframes from a dict
with arrays of different lengths.所以现在我们需要一种能够从具有不同长度数组的dict
创建数据帧的方法。 For that, we can:为此,我们可以:
df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in dictA.items()])) print(df)
Output:输出:
ABC 0 [1, 2, 3] [4, 4, 4] [4, 6, 0] 1 [1, 2, 3] [4, 4, 4] NaN 2 [1, 2, 3] NaN NaN
We want the dataframe to be vertical, so for each iteration, flatten out the lists with a list comprehension:我们希望数据框是垂直的,因此对于每次迭代,使用列表理解将列表展平:
df = pd.DataFrame(dict([(k, pd.Series([a for b in v for a in b])) for k, v in dictA.items()])) print(df)
Output:输出:
ABC 0 1 4.0 4.0 1 2 4.0 6.0 2 3 4.0 0.0 3 1 4.0 NaN 4 2 4.0 NaN 5 3 4.0 NaN 6 1 NaN NaN 7 2 NaN NaN 8 3 NaN NaN
Now we want to replace all the NaN
s with blanks.现在我们想用空格替换所有的NaN
。 For that, we need to import numpy as np
, and do:为此,我们需要import numpy as np
,然后执行:
df = pd.DataFrame(dict([(k, pd.Series([a for b in v for a in b])) for k, v in dictA.items()])).replace(np.nan, '') print(df)
Output:输出:
ABC 0 1 4 4 1 2 4 6 2 3 4 0 3 1 4 4 2 4 5 3 4 6 1 7 2 8 3
Finally use formatted string to convert the letters into "Col"
letters:最后使用格式化字符串将字母转换为"Col"
字母:
df = pd.DataFrame(dict([(f'Col{k}', pd.Series([a for b in v for a in b])) for k,v in dictA.items()])).replace(np.nan, '') print(df)
Output:输出:
ColA ColB ColC 0 1 4 4 1 2 4 6 2 3 4 0 3 1 4 4 2 4 5 3 4 6 1 7 2 8 3
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