如何返回熊猫数据框中元素的索引值

Question

I have a dataframe of corporate actions for a specific equity. it looks something like this:

0             Declared Date       Ex-Date    Record Date
BAR_DATE                 
2018-01-17       2017-02-21    2017-08-09     2017-08-11
2018-01-16       2017-02-21    2017-05-10     2017-06-05

except that it has hundreds of rows, but that is unimportant. I created the index "BAR_DATE" from one of the columns which is where the 0 comes from above BAR_DATE.

What I want to do is to be able to reference a specific element of the dataframe and return the index value, or BAR_DATE, I think it would go something like this:

index_value = cacs.iloc[5, :].index.get_values()

except index_value becomes the column names, not the index. Now, this may stem from a poor understanding of indexing in pandas dataframes, so this may or may not be really easy to solve for someone else.

I have looked at a number of other questions including this one , but it returns column values as well.

Answer 1

Your code is really close, but you took it just one step further than you needed to.

# creates a slice of the dataframe where the row is at iloc 5 (row number 5) and where the slice includes all columns
slice_of_df = cacs.iloc[5, :]

# returns the index of the slice
# this will be an Index() object
index_of_slice = slice_of_df.index

From here we can use the documentation on the Index object: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.html

# turns the index into a list of values in the index
index_list = index_of_slice.to_list()

# gets the first index value
first_value = index_list[0]

The most important thing to remember about the Index is that it is an object of its own, and thus we need to change it to the type we expect to work with if we want something other than an index. This is where documentation can be a huge help.

EDIT: It turns out that the iloc in this case is returning a Series object which is why the solution is returning the wrong value. Knowing this, the new solution would be:

# creates a Series object from row 5 (technically the 6th row)
row_as_series = cacs.iloc[5, :]

# the name of a series relates to it's index
index_of_series = row_as_series.name

This would be the approach for single-row indexing. You would use the former approach with multi-row indexing where the return value is a DataFrame and not a Series .

Unfortunately, I don't know how to coerce the Series into a DataFrame for single-row slicingbeyond explicit conversion:

row_as_df = DataFrame(cacs.iloc[5, :])

While this will work, and the first approach will happily take this and return the index, there is likely a reason why Pandas doesn't return a DataFrame for single-row slicing so I am hesitant to offer this as a solution.