Turn the Column Headers into the First Row and Row Headers into the First Column in Pandas Dataframe

Turn the column headers into the first row and row headers into the first column in Pandas dataframe

for your solution, you can just shift it. But if you are reading the data from any csv file, while reading you can take considerations of not taking header(header = None)

    345 456 789
123         
987 876 765 543

df.reset_index().T.reset_index().T

Out:

         0  1   2   3
index   123 345 456 789
0       987 876 765 543


pd.read_csv('data.csv',header=None)

Out:

    0   1   2   3
0   123 345 456 789
1   987 876 765 543

How to set column headers to the first row in Pandas dataframe?

You can use np.vstack on a list of column names and the DataFrame to create an array with one extra row; then cast it into pd.DataFrame:

out = pd.DataFrame(np.vstack([df.columns, df]))

Output:

   0  1  2
0  A  B  C
1  1  2  3
2  4  5  6
3  7  8  9

move column names to first row in pandas frame

One line DataFrame.T + DataFrame.reset_index(). You can set the names of columns with DataFrame.set_axis()

new_df = (df.T.reset_index().T.reset_index(drop=True)
            .set_axis([f'Q1.{i+1}' for i in range(df.shape[1])], axis=1))
print(new_df)

Output

  Q1.1 Q1.2 Q1.3 Q1.4 Q1.5
0    a    b    c    d    e
1    1    2    2    2    2
2    2    3    3    3    3
3    3    4    4    4    4
4    4    5    5    5    5
5    5    6    6    6    6
6    6    7    7    7    7

Python Pandas Replacing Header with Top Row

new_header = df.iloc[0] #grab the first row for the header
df = df[1:] #take the data less the header row
df.columns = new_header #set the header row as the df header

Convert row to column header for Pandas DataFrame,

In [21]: df = pd.DataFrame([(1,2,3), ('foo','bar','baz'), (4,5,6)])

In [22]: df
Out[22]: 
     0    1    2
0    1    2    3
1  foo  bar  baz
2    4    5    6

Set the column labels to equal the values in the 2nd row (index location 1):

In [23]: df.columns = df.iloc[1]

If the index has unique labels, you can drop the 2nd row using:

In [24]: df.drop(df.index[1])
Out[24]: 
1 foo bar baz
0   1   2   3
2   4   5   6

If the index is not unique, you could use:

In [133]: df.iloc[pd.RangeIndex(len(df)).drop(1)]
Out[133]: 
1 foo bar baz
0   1   2   3
2   4   5   6

Using df.drop(df.index[1]) removes all rows with the same label as the second row. Because non-unique indexes can lead to stumbling blocks (or potential bugs) like this, it's often better to take care that the index is unique (even though Pandas does not require it).

Python: How do I 'demote' a column header to be a row and rename column headers (without replacing the original column headers)?

As mentioned in the comment, if you are reading CSV file, you can pass the column names:

df = pd.read_csv(filePath, names=["Faculty", "Male", "Female", "Total"])

But if you already have the dataframe, you can add the columns to index at -1, then you can reset the index, you can also sort the index if needed.:

df.loc[-1] = df.columns.to_list() 
df = df.sort_index().reset_index(drop=True)
df.columns = ['Faculty', 'Male', 'Female', 'Totals']

Turn column headers into the first row of my df

I think you need header=None:

pd.read_html(route, flavor='html5lib',header=None)

Merge the first row with the column headers in a dataframe

I think you need numpy.concatenate, similar principe like cᴏʟᴅsᴘᴇᴇᴅ answer:

df.columns = np.concatenate([df.iloc[0, :2], df.columns[2:]])
df = df.iloc[1:].reset_index(drop=True)
print (df)
  Sample type Concentration     A     B    C          D          E          F  \
0       Water          9200  95.5  21.0  6.0  11.942308  64.134615  21.498560   
1       Water          9200  94.5  17.0  5.0   5.484615  63.205769  19.658560   
2       Water          9200  92.0  16.0  3.0  11.057692  62.586538  19.813120   
3       Water          4600  53.0   7.5  2.5   3.538462  35.163462   6.876207   

          G         H  
0  5.567840  1.174135  
1  4.968000  1.883444  
2  5.192480  0.564835  
3  1.641724  0.144654  

First row to header with pandas

what about defining that when you load your table in the first place?

pd.read_csv('filename', header = 1)

otherwise I guess you can just do this:

df.drop('0', axis = 1)

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