索引 Pandas Dataframe 混合行号和列名

Question

Coming from R and finding the index rules for pandas dataframes to be not easy to use. I have a dataframe where I want to get the ith row and some columns by their names. I can clearly understand using either iloc or loc as shown below.

df = pd.DataFrame(np.random.randn(8, 4),columns=['A', 'B', 'C', 'D'])
df.loc[:,['A', 'B']]
df.iloc[0:,0:2]

Conceptually what I want is something like:

df.loc[0:,['A', 'B']]

Meaning the first row with those columns. Of course that code fails. I can seemingly use:

df.loc[0:0,['A', 'B']]

But, this seems strange, though it works. How does one properly index using a combination of row number and column names? In R we would do something like:

df = data.frame(matrix(rnorm(32),8,4))
colnames(df) <- c("A", "B", "C", "D") 
df[1, c('A', 'B')]

*** UPDATE *** I was mistaken, the example code above indeed works on this toy dataframe. But, on my real data, I see the following? Both objects are of same type and code is the same, not understanding the error here.

type(poly_set)
<class 'pandas.core.frame.DataFrame'>
poly_set.loc[:,['P1', 'P2', 'P3']]
                      P1            P2           P3
29   -2.0897226679999998  -1.237649556         None
361  -2.0789117340000001   0.144751427  1.572417454
642  -2.0681314259999999  -0.196563749  1.500834574

poly_set.loc[0,['P1', 'P2', 'P3']]
Traceback (most recent call last):
  File "C:\Users\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\indexes\base.py", line 2646, in get_loc
    return self._engine.get_loc(key)
  File "pandas\_libs\index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 998, in pandas._libs.hashtable.Int64HashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1005, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 0

Answer 1

You are using slicing which means between two given index. If you only want first row data just use:

Try:

df = df.reset_index()    
df.loc[0,['A', 'B']]

Answer 2

You can use .iloc (to get the i-th row) and .loc (to get columns by name) together:

row_number = 0
df.iloc[row_number].loc[['A', 'B']]

You can even remove the .loc :

df.iloc[row_number][['A', 'B']]

Answer 3

I agree that pandas slicing rules are not as easy to use as they should be. I believe the suggested approach these days is to use loc[] with a nested index lookup

df.loc[df.index[row_numbers], ['A','B']]

I have no idea why pandas still does not have an xloc[] or something similar that allows for row numbers and column names. See this answer to the same question.

In your answer update, you use loc[] , which can only look up row and column indexes , but you can see from the previous printout that there is no row with an index of 0. The row that is in location 0 has an index of 29. If you use my approach or the others mentioned here, you will have success.