Convert Row to Column Header for Pandas Dataframe,

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 Pandas: Convert Rows as Column headers

You're looking for pivot_table:

In [11]: medals = df.pivot_table('no of medals', ['Year', 'Country'], 'medal')

In [12]: medals
Out[12]:
medal             Bronze  Gold  Silver
Year Country
1896 Afghanistan       3     5       4
     Algeria           3     1       2

and if you want to reorder the columns:

In [12]: medals.reindex_axis(['Gold', 'Silver', 'Bronze'], axis=1)
Out[12]:
medal             Gold  Silver  Bronze
Year Country
1896 Afghanistan     5       4       3
     Algeria         1       2       3

Convert combinations of row+column as Column headers

Let us do pivot_table then swaplevel

s = df.pivot_table(index= ['Machine','Time'], 
                   columns = df.Part.astype(str).radd('Part'),
                   values=['PowerA','PowerB'],
                   fill_value=-1).swaplevel(1,0, axis=1).sort_index(level=0, axis=1)

s.columns = s.columns.map('_'.join)
s.reset_index(inplace=True)
s
Out[751]: 
   Machine   Time  Part1_PowerA  Part1_PowerB  Part2_PowerA  Part2_PowerB
0        1  20:30           0.1           0.4           0.9           0.7
1        1  20:31           0.3           0.1           0.2           0.3
2        2  20:30           0.2           0.5          -1.0          -1.0
3        2  20:31           0.8           0.4          -1.0          -1.0

How to Convert rows into columns headers and values of other column as data?

I think you need convert Name to index, select by double [] for one column DataFrame and transpose:

df1 = df.set_index('Name')[['Data']].T

Using Pandas convert column values to column header

I think code below will help you:

# create your processing function
def handle_function(row: pd.Series) -> pd.Series:
    """
    What you want to do here...
    """

    # get label and value
    label = row["field_label"]
    value = row["field_value"]
    
    # create column for label if it exists no create
    row[label] = value
   
    # return new row
    return row

# apply on your dataframe
df.apply(handle_function, axis=1)

# remove columns for label and values
df.drop(["field_label", "field_value", axis=1, inplace=True]

Convert first row of pandas dataframe to column name

I believe need to add parameter to read_html:

df = pd.read_html(url, header=1)[0]

Or:

df = pd.read_html(url, skiprows=1)[0]

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

How can I create column headers from row values in a slice of columns resulting in the column row values matching the column headers?

The idea is to convert the DataFrame to the long form and then pivot:

df = df.reset_index()
df = pd.melt(df, id_vars=['index']).pivot(index = 'index', values='variable', columns='value')
for c in df.columns:
    df.loc[df[c].notna(), c] = c

value   bar baz egg foo ham
index                   
0       bar NaN NaN foo ham
1       bar baz NaN foo NaN
2       NaN baz egg NaN ham

Detailed explanation:

melt would convert the DataFrame to the following form:

   index    variable    value
0   0   A   foo
1   1   A   baz
2   2   A   ham
3   0   B   bar
4   1   B   foo
5   2   B   baz
6   0   C   ham
7   1   C   bar
8   2   C   egg

Then use pivot to make the columns to be all the unique values:

value   bar baz egg foo ham
index                   
0   B   NaN NaN A   C
1   C   A   NaN B   NaN
2   NaN B   C   NaN A

Then simply replace all non-na columns to be the column name.

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