Binning error for a dataframe column – KeyError: "None of [Float64Index([61.5, 59.8, 56.8…. dtype='float64', length=53940)] are in the [columns]"

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英文:

Binning error for a dataframe column - KeyError: "None of [Float64Index([61.5, 59.8, 56.8.... dtype='float64', length=53940)] are in the [columns]"

问题

  1. 我为数据框列编写了一个用于分箱数值的函数，即将列值分成指定数量的类别。
  3.     def binning_fun(df, col_name, num_of_bins):
  4.         lt=[]
  5.         for i in range(0,num_of_bins):
  6.             lt.append(i)
  7.             df[col_name]=pd.cut(df[col_name],bins=i+1, labels=lt)
  8.         return df
  9.     
  10.     df="C:/Users/shootings.csv"
  11.     binning_fun(df, df['depth'], 4)
  13. 这导致以下错误：
  15. **"None of [Float64Index([61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4,
  16.               ...
  17.               60.5, 59.8, 60.5, 61.2, 62.7, 60.8, 63.1, 62.8, 61.0, 62.2],
  18.              dtype='float64', length=53940)] are in the [columns]"**
  20. 这些值确实存在于 'depth' 列中。为什么会被认为不存在呢？
  22. 我的数据集：
  24.         carat   cut     clarity     depth   table
  25.     0   0.23    Ideal   SI2         61.5    55.0
  26.     1   0.21    Premium SI1         59.8    61.0
  27.     2   0.23    Good    VS1         56.9    65.0
  28.     3   0.29    Premium VS2         62.4    58.0
  29.     4   0.31    Good    SI2         63.3    58.0
  30.     5   0.24    Good    VVS2 90.7  62.8
  32. 预期输出：
  34.     depth
  35.     1
  36.     0
  37.     0
  38.     1
  39.     1
  40.     2
英文:

I wrote a function for binning the numerical values of a dataframe column, i.e., dividing the column values into the specified number of categories.

  1. def binning_fun(df, col_name, num_of_bins):
  2.     lt=[]
  3.     for i in range(0,num_of_bins):
  4.         lt.append(i)
  5.         df[col_name]=pd.cut(df[col_name],bins=i+1, labels=lt)
  6.     return df
  8. df="C:/Users/shootings.csv"
  9. binning_fun(df, df['depth'], 4)

This gives the following error:

"None of [Float64Index([61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4,\n ...\n 60.5, 59.8, 60.5, 61.2, 62.7, 60.8, 63.1, 62.8, 61.0, 62.2],\n dtype='float64', length=53940)] are in the [columns]"

These values do exist in the column 'depth'. Why are they being called inexistent?

My dataset:

  1. 		carat	cut		clarity	 depth	table	
  2. 0   	0.23	Ideal		SI2	 61.5	55.0	
  3. 1		0.21	Premium		SI1	 59.8	61.0	
  4. 2		0.23	Good		VS1	 56.9	65.0	
  5. 3		0.29	Premium		VS2	 62.4	58.0	
  6. 4		0.31	Good		SI2	 63.3	58.0	
  7. 5		0.24	Good		VVS2 90.7	62.8

Expected output:

  1. depth
  2. 1
  3. 0
  4. 0
  5. 1
  6. 1
  7. 2

答案1

得分: 1

你可以使用 cut 来获得固定的箱体大小：

  1. def binning_fun(df, col_name, num_of_bins):
  2.     df[col_name] = pd.cut(df[col_name], bins=num_of_bins, labels=range(num_of_bins))
  3.     return df
  5. df = pd.read_csv("C:/Users/shootings.csv")
  6. binning_fun(df, 'depth', 4)

输出：

  1.     carat   cut     clarity     depth   table
  2. 0   0.23    Ideal   SI2         0       55.00
  3. 1   0.21    Premium SI1         0       61.00
  4. 2   0.23    Good    VS1         0       65.00
  5. 3   0.29    Premium VS2         0       58.00
  6. 4   0.31    Good    SI2         0       58.00
  7. 5   0.24    Good    VVS2        3       62.80

或者使用 qcut 来获得等大小的桶：

  1. def binning_fun(df, col_name, num_of_bins):
  2.     df[col_name] = pd.qcut(df[col_name], q=num_of_bins, labels=range(num_of_bins))
  3.     return df
  5. df = pd.read_csv("C:/Users/shootings.csv")
  6. binning_fun(df, 'depth', 4)

输出：

  1.     carat   cut     clarity     depth   table
  2. 0   0.23    Ideal   SI2         1       55.00
  3. 1   0.21    Premium SI1         0       61.00
  4. 2   0.23    Good    VS1         0       65.00
  5. 3   0.29    Premium VS2         2       58.00
  6. 4   0.31    Good    SI2         3       58.00
  7. 5   0.24    Good    VVS2        3       62.80

希望这有所帮助。

英文:

You can use cut for fixed bin sizes:

  1. def binning_fun(df, col_name, num_of_bins):
  2.     df[col_name]=pd.cut(df[col_name], bins=num_of_bins, labels=range(num_of_bins))
  3.     return df
  5. df = pd.read_csv("C:/Users/shootings.csv")
  6. binning_fun(df, 'depth', 4)

Output:

  1.     carat	cut	    clarity	 depth	 table
  2. 0	0.23	Ideal	SI2	     0	     55.00
  3. 1	0.21	Premium	SI1  	 0	     61.00
  4. 2	0.23	Good	VS1	     0       65.00
  5. 3	0.29	Premium	VS2	     0       58.00
  6. 4	0.31	Good	SI2	     0       58.00
  7. 5	0.24	Good	VVS2	 3	     62.80

Or use qcut for equal-sized buckets:

  1. def binning_fun(df, col_name, num_of_bins):
  2.     df[col_name]=pd.qcut(df[col_name], q=num_of_bins, labels=range(num_of_bins))
  3.     return df
  5. df=pd.read_csv("C:/Users/shootings.csv")
  6. binning_fun(df, 'depth', 4)

Output:

  1.     carat	cut	    clarity	depth	table
  2. 0	0.23	Ideal	SI2	    1	    55.00
  3. 1	0.21	Premium	SI1	    0	    61.00
  4. 2	0.23	Good	VS1	    0	    65.00
  5. 3	0.29	Premium	VS2	    2	    58.00
  6. 4	0.31	Good	SI2	    3	    58.00
  7. 5	0.24	Good	VVS2	3	    62.80

I hope this helps.

huangapple
  • 本文由 发表于 2023年2月8日 14:56:02
  • 转载请务必保留本文链接：https://go.coder-hub.com/75382294.html
  • binning
  • dataframe
  • pandas
  • python
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