Pandas DataFrame列分离

Question

I have a large DataFrame, in which there is only one column with all the values. I need to separate the data into more columns. After a lot of trial and error, I gave up and sought to your help.

the head of the DataFrame looks like this: the rows are a Series object. not values

                                                        column1
    ---------------------------------------------------------------------
    MultiIndex1  | 1.00   2.00   3.00   4.00   5.00   6.00   7.00
                 | 1.00   2.00   3.00   4.00   5.00   6.00   7.00
                 | 1.00   2.00   3.00   4.00   5.00   6.00   7.00
                 | 1.00   2.00   3.00   4.00   5.00   6.00   7.00
                 | 1.00   2.00   3.00   4.00   5.00   6.00   7.00
                 | 1.00   2.00   3.00   4.00   5.00   6.00   7.00

my desired output should look like this:

                 column1|column2|column3|column4|column5|column6|column7
    ---------------------------------------------------------------------
    MultiIndex1  | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00
                 | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00
                 | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00
                 | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00
                 | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00
                 | 1.00 |  2.00 |  3.00 |  4.00 |  5.00 |  6.00 |  7.00

i've tried to: df.columns = ['col1','col2','col3','col4','col5'...]

i've tried turning it into a series and back to a df.

tried applying .str.split functions.

lots of slicing and concat, but no success.

help would be much appreciated. Thanks!

here is the first few lines of my dataset, as an example:

the date and AALR3 are the Row MultiIndex

2019-01-02;AALR3 ;0000000020; 000000000013.300000;000000000000000100;10:00:04.961;1;2019-01-02;000086597137782;000000000310091;2;2019-01-02;000086597142909;000000000310092;1;0;00000072;00000174 2019-01-02;AALR3 ;0000000010; 000000000013.310000;000000000000003000;10:00:04.961;1;2019-01-02;000086597135827;000000000310088;2;2019-01-02;000086597142909;000000000310089;1;0;00000120;00000174 2019-01-02;AALR3 ;0000000050; 000000000013.390000;000000000000000200;10:11:40.214;1;2019-01-02;000086597182855;000000000400273;1;2019-01-02;000086597151579;000000000400274;2;0;00000058;00000008 2019-01-02;AALR3 ;0000000040; 000000000013.380000;000000000000000100;10:11:40.214;1;2019-01-02;000086597182855;000000000400271;1;2019-01-02;000086597151578;000000000400272;2;0;00000058;00000174 2019-01-02;AALR3 ;0000000030; 000000000013.380000;000000000000000100;10:11:40.214;1;2019-01-02;000086597182855;000000000400269;1;2019-01-02;000086597151189;000000000400270;2;0;00000058;00000308

im reading it with:

    pd.read_csv('//path_to_file', sep=';')

I want to name the columns like this.

    df.columns = ['Session Date','Instrument Symbol','Trade Number','Trade Price','Traded Quantity',
          'Trade Time','Trade Indicator','Buy Order Date','Sequential Buy Order Number',
          'Secondary Order ID - Buy Order','Aggressor Buy Order Indicator','Sell Order Date',
         'Sequential Sell Order Number','Secondary Order ID - Sell Order','Aggressor Sell Order Indicator',
          'Cross Trade Indicator','Buy Member','Sell Member']

UPDATE:

the solutions were effective, thank you very much.

I is almost the way i want it. Is there a way to make the duplicate indexes a MultiIndex as well? I managed to make the dates, but not the symbol. Thanks

Answer 1

Make a try with this-

your_df = pd.DataFrame(df.column1.str.split(' ',1).tolist(), columns = ['col1','col2','col3','col4','col5','col6','col7'])
print(your_df)

Answer 2

What you are seeing is a MultiIndex Dataframe , and what you are looking for a SingleIndex dataframe , Try

df = df.reset_index()
df.columns = ['col1','col2','col3','col4','col5','col6','col7']