Reorder one or several columns of a dataframe #pandas

change_column_order

def change_column_order(df, col_position):
    '''Reorder one or several columns of a dataframe.
    
    Based on the function described at https://stackoverflow.com/a/37071454.

    Parameters
    ----------
    df : pandas dataframe
    col_position : dict
        {'Column' : index where the column should be}

    Returns
    -------
    df
        The daframe with columns reordered.
    '''
    
    def reorder(df, col_name, idx):
        cols = df.columns.tolist()
        cols.remove(col_name)
        
        cols.insert(idx, col_name)
        return df[cols]

    for key, value in col_position.items():
        col_name = key
        idx = value
        df = reorder(df, col_name, idx)
        
    return df
    
# Other ways to reorder columns
## Reordering Pandas DataFrame Columns
 df = df[['names','Score','Room','Grade','Teacher','Hobby']]
 
## Leveraging handy Pandas methods
### Another standard solution to is to pair the pd.drop() and the pd.insert() methods. The main limitation of this option is that it only works when moving one column at a time to a specific location.

mid = df['Hobby']
df.drop(labels=['Hobby'], axis=1, inplace = True)
df.insert(1, 'Hobby', mid)


## Another function using a reference column to position other columns 
def movecol(df, cols_to_move=[], ref_col='', place='After'):
    
    cols = df.columns.tolist()
    if place == 'After':
        seg1 = cols[:list(cols).index(ref_col) + 1]
        seg2 = cols_to_move
    if place == 'Before':
        seg1 = cols[:list(cols).index(ref_col)]
        seg2 = cols_to_move + [ref_col]
    
    seg1 = [i for i in seg1 if i not in seg2]
    seg3 = [i for i in cols if i not in seg1 + seg2]
    
    return(df[seg1 + seg2 + seg3])