CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
MattR
MattR

Reputation: 5126

Printing Column Names and Values in Dataframe

The end goal of this question is to plot X and Y for a graph using a dataframe.

I have a dataframe like so:

             Open    High     Low   Close     Volume       stock symbol
Date                                                              
2000-10-19    1.37    1.42    1.24    1.35  373590000         AAPL
2000-10-20    1.36    1.46    1.35    1.39  195836200         AAPL
2000-10-23    1.39    1.49    1.39    1.46  129851400         AAPL
2000-10-24    1.48    1.49    1.34    1.35  192711400         AAPL
2000-10-25    1.36    1.37    1.30    1.32  163448600         AAPL
2000-10-26    1.34    1.42    1.25    1.32  178110800         AAPL
2000-10-27    1.35    1.37    1.28    1.33  181242600         AAPL
2000-10-30    1.37    1.42    1.34    1.38  152558000         AAPL

And I am trying to plot Date vs. Open. I know there is a way to simply plot, but I will be applying this concept to larger dataframes and would like to know how to do it "long-hand".

What I've tried:

print(some_DF['Open'])

Result: 

 Date
    2000-10-19      1.37
    2000-10-20      1.36
    2000-10-23      1.39
    2000-10-24      1.48
    2000-10-25      1.36
    2000-10-26      1.34

Problem:

Date seems to be my index, but the column header 'Open' Does not appear.

Question:

How do i print the above Dataframe while having 'Open' as my header. Then making some value x=Date's column and some value y = 'Open's values?

"Expected Code to work":

Im thinking something like

print([some_DF['Open'] headers = 'date','open')
x = some_DF['Date'] #So that this becomes first column of dataframe
y = some_DF['Open'] #So that this becomes second column of dataframe

Upvotes: 0

Views: 35509

Answers (1)

Asish M.
Asish M.

Reputation: 2647

You can reset_index on the data-frame and then print the subset dataframe consisting of the two columns

>>> df
            a  b
Date            
2000-10-19  1  3
2000-10-20  2  4
2000-10-21  3  5
2000-10-22  4  6
2000-10-23  5  7
>>> print(df.reset_index()[['Date', 'a']])
        Date  a
0 2000-10-19  1
1 2000-10-20  2
2 2000-10-21  3
3 2000-10-22  4
4 2000-10-23  5

Like IanS mentioned, you shouldn't worry about how the output looks in pandas. Date was an index and Open a column. The difference in the print statement illustrates that distinction.

Edit:

The df[[list_of_column_names]] is the same as df.loc[:, [list_of_column_names]]. It gives a list of columns to subset the original dataframe.

Upvotes: 4

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