pandas python熊猫复数
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python pandas complex number
提问by Dimitris
I am using pandas which very efficiently sorts/filters the data they way I need.
我正在使用 Pandas，它可以非常有效地按照我需要的方式对数据进行排序/过滤。
This code worked fine, until I changed the last column to a complex number; now I get an error.
这段代码运行良好，直到我将最后一列更改为复数；现在我收到一个错误。
return self._cython_agg_general('mean') raise DataError('No numeric types to aggregate') pandas.core.groupby.DataError: No numeric types to aggregate
return self._cython_agg_general('mean') raise DataError('No numeric types to aggregate') pandas.core.groupby.DataError：没有要聚合的数字类型
The error refers to my eighth column (with the complex numbers) since I want the mean value I cannot find a way to convert the object to a complex number (from what I understand pandas now support complex numbers).
该错误是指我的第八列（带有复数），因为我想要平均值我找不到将对象转换为复数的方法（据我所知，Pandas现在支持复数）。
This is the code I use.
这是我使用的代码。
import numpy as np
import pandas as pd
df = pd.read_csv('final.dat', sep=",", header=None)
df.columns=['X.1', 'X.2', 'X.3', 'X.4','X.5', 'X.6', 'X.7', 'X.8']
df1 = df.groupby(["X.1","X.2","X.5"])["X.8"].mean().reset_index()
After that I get the error described above.
之后，我收到上述错误。
When I read my file, this is the df output.
当我阅读我的文件时，这是 df 输出。
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21266 entries, 0 to 21265
Data columns (total 8 columns):
X.1 21266 non-null values
X.2 21266 non-null values
X.3 21266 non-null values
X.4 21266 non-null values
X.5 21266 non-null values
X.6 21266 non-null values
X.7 21266 non-null values
X.8 21266 non-null values
dtypes: float64(4), int64(3), object(1)
This is a small sample of the input file.
这是输入文件的一个小样本。
回答by Jeff
The parse doesn't support reading of complex directly, so do the following transform.
解析不支持直接读取复数，因此请执行以下转换。
In [37]: df['X.8'] = df['X.8'].str.replace('i','j').apply(lambda x: np.complex(x))
In [38]: df
Out[38]:
X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8
0 564991.15 7371277.89 0 1 1530 0.1 2 (92.289+151.96j)
1 564991.15 7371277.89 0 1 8250 0.1 2 (104.22-43.299j)
2 564991.15 7371277.89 0 1 20370 0.1 2 (78.76-113.52j)
3 564991.15 7371277.89 0 1 33030 0.1 2 (27.141-154.1j)
4 564991.15 7371277.89 0 1 47970 0.1 2 (-30.012-175j)
5 564991.15 7371277.89 0 1 63090 0.1 2 (-118.52-342.43j)
6 564991.15 7371277.89 0 1 93090 0.1 2 (-321.02-1541.5j)
7 564991.15 7371277.89 0 2 1530 0.1 2 (118.73+154.05j)
8 564991.15 7371277.89 0 2 8250 0.1 2 (122.13-45.571j)
9 564991.15 7371277.89 0 2 20370 0.1 2 (93.014-116.03j)
10 564991.15 7371277.89 0 2 33030 0.1 2 (38.56-155.08j)
11 564991.15 7371277.89 0 2 47970 0.1 2 (-20.653-173.83j)
12 564991.15 7371277.89 0 2 63090 0.1 2 (-118.41-340.58j)
13 564991.15 7371277.89 0 2 93090 0.1 2 (-378.71-1554j)
14 564990.35 7371279.17 0 1785 1530 0.1 2 (-15.441+118.3j)
15 564990.35 7371279.17 0 1785 8250 0.1 2 (-7.1735-76.487j)
16 564990.35 7371279.17 0 1785 20370 0.1 2 (-33.847-145.99j)
17 564990.35 7371279.17 0 1785 33030 0.1 2 (-86.035-185.46j)
18 564990.35 7371279.17 0 1785 47970 0.1 2 (-143.37-205.23j)
19 564990.35 7371279.17 0 1785 63090 0.1 2 (-234.67-370.43j)
20 564990.35 7371279.17 0 1785 93090 0.1 2 (-458.69-1561.4j)
21 564990.36 7371279.17 0 1786 1530 0.1 2 (36.129+128.4j)
22 564990.36 7371279.17 0 1786 8250 0.1 2 (39.406-69.607j)
23 564990.36 7371279.17 0 1786 20370 0.1 2 (10.495-139.48j)
24 564990.36 7371279.17 0 1786 33030 0.1 2 (-43.535-178.19j)
25 564990.36 7371279.17 0 1786 47970 0.1 2 (-102.28-196.76j)
26 564990.36 7371279.17 0 1786 63090 0.1 2 (-199.32-362.1j)
27 564990.36 7371279.17 0 1786 93090 0.1 2 (-458.09-1565.6j)
In [39]: df.dtypes
Out[39]:
X.1 float64
X.2 float64
X.3 float64
X.4 int64
X.5 int64
X.6 float64
X.7 int64
X.8 complex128
dtype: object
In [40]: df1 = df.groupby(["X.1","X.2","X.5"])["X.8"].mean().reset_index()
In [41]: df.groupby(["X.1","X.2","X.5"])["X.8"].mean().reset_index()
Out[41]:
X.1 X.2 X.5 X.8
0 564990.35 7371279.17 1530 (-15.441+118.3j)
1 564990.35 7371279.17 8250 (-7.1735-76.487j)
2 564990.35 7371279.17 20370 (-33.847-145.99j)
3 564990.35 7371279.17 33030 (-86.035-185.46j)
4 564990.35 7371279.17 47970 (-143.37-205.23j)
5 564990.35 7371279.17 63090 (-234.67-370.43j)
6 564990.35 7371279.17 93090 (-458.69-1561.4j)
7 564990.36 7371279.17 1530 (36.129+128.4j)
8 564990.36 7371279.17 8250 (39.406-69.607j)
9 564990.36 7371279.17 20370 (10.495-139.48j)
10 564990.36 7371279.17 33030 (-43.535-178.19j)
11 564990.36 7371279.17 47970 (-102.28-196.76j)
12 564990.36 7371279.17 63090 (-199.32-362.1j)
13 564990.36 7371279.17 93090 (-458.09-1565.6j)
14 564991.15 7371277.89 1530 (105.5095+153.005j)
15 564991.15 7371277.89 8250 (113.175-44.435j)
16 564991.15 7371277.89 20370 (85.887-114.775j)
17 564991.15 7371277.89 33030 (32.8505-154.59j)
18 564991.15 7371277.89 47970 (-25.3325-174.415j)
19 564991.15 7371277.89 63090 (-118.465-341.505j)
20 564991.15 7371277.89 93090 (-349.865-1547.75j)
回答by Viktor Kerkez
Or you can parse it directly as a complex number by passing in a converter for that column when you read in the data:
或者，您可以通过在读取数据时传入该列的转换器来直接将其解析为复数：
pd.read_csv('final.dat', header=None,
names=['X.1', 'X.2', 'X.3', 'X.4','X.5', 'X.6', 'X.7', 'X.8'],
converters={'X.8': lambda s: np.complex(s.replace('i', 'j'))})
回答by athlonshi
For converting all columns, try pd.applymap(lambda s: np.complex(s.replace('i', 'j')))
要转换所有列，请尝试 pd.applymap(lambda s: np.complex(s.replace('i', 'j')))
回答by Brad123
I tried implementing the lambda but was getting a error:
我尝试实现 lambda，但出现错误：
ValueError: complex() arg is a malformed string
ValueError: complex() arg 是一个格式错误的字符串
I found out I had to eliminate the spaces as well as change the 'i' character to 'j' Here's my code:
我发现我必须消除空格并将“i”字符更改为“j”这是我的代码：
for tits in df.columns:
if df[tits].dtypes =='O':
df[tits] = df[tits].str.replace('i','j')
df[tits] = df[tits].str.replace(' ','')
df[tits] = df[tits].apply(lambda x: np.complex(x))
print(df[df.columns[1]])
print(df.dtypes)
