使用其他两列中的数组创建新列，并在python pandas中对其进行测试

tomasz74

我尝试创建一个以点为坐标的列作为一个numpy数组。我有一个数据，如东和北。我想简单地通过将其向下移动来减少大量数据。我尝试用Unittest进行测试

我尝试遵循其他问题，.apply(lambda)但可以解决我的问题。（我在pandas 0.9中工作，无法更新）。下面是一个示例代码，而我苦苦挣扎的功能是adjustCoordSystem()

import unittest
import pandas as pd
from pandas.util.testing import assert_frame_equal

def exampleDf():
    df = pd.DataFrame({'Easting':{0:11,1:12,2:13,3:14},
                  'Northing':{0:5,1:7,2:9,3:11}})
    return df

def exampWithCoord():
    df = exampleDf()
    df['Sample']=[[0,0,0],[1,2,0],[2,4,0],[3,6,0]]
    return df

class dfProccesedFull():

    def adjustCoordSystem(self, df):
        ''' change coordinate system to get from 0 to max'''
        df['Sample'] = \
        [df['Easting'].apply(lambda x: x - min(df['Easting'])),
         df['Northing'].apply(lambda x: x - min(df['Northing'])),
         df['Northing'].apply(lambda x: 0.0)]

#         [(df['Easting'] - min(df['Easting'])), (df['Northing'] - min(df['Northing'])),\
#          df['Northing'].apply(lambda x: 0.0)]

        return df

class TestDfProccesedDataFull(unittest.TestCase):

    def test_adjustCoordSystem(self):
        df = exampleDf()
        dfModel = exampWithCoord()
        tData =  dfProccesedFull()
        dfTested=tData.adjustCoordSystem(df)
        assert_frame_equal(dfTested, dfModel)

if __name__ == "__main__"
    unittest.main()

我有一个错误：AssertionError行：df['Northing'].apply(lambda x: 0.0)]

如何更改函数以在“示例”列中具有数组列表，但不遍历每一行？

我正在寻找的输出是新的数据框，例如：

   Easting  Northing     Sample
0       11         5  [0, 0, 0]
1       12         7  [1, 2, 0]
2       13         9  [2, 4, 0]
3       14        11  [3, 6, 0]

其中“样本”列为[从Easting开始的x坐标，从Northing开始的y坐标，z坐标= 0]

安迪·海登（Andy Hayden）

我不确定这是什么意思...您正在尝试将其分配给单个列，除非df的长度为三，否则它将失败：

df['Sample'] = [df['Easting'].apply(lambda x: x - min(df['Easting'])),
                df['Northing'].apply(lambda x: x - min(df['Northing'])),
                df['Northing'].apply(lambda x: 0.0)]

参见例如：

In [21]: df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])

In [22]: df['C'] = [df.copy(), df.copy()]  # use copy to avoid max recursion error...

In [23]: df['C'] = [1, 2, 3]
ValueError: Length of values does not match length of index

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