pythonic way to parse/split URLs in a pandas dataframe

import csv

data = StringIO(
'''
"XAU=,XAU=,XAG=,XAG="  
"25/08/2014 6:00:05,1200.343,25/08/2014 6:00:03,19.44,"            
"25/08/2014 6:00:05,1200,,,"
''')

df = pd.read_csv(data, quoting=csv.QUOTE_NONE, index_col=False, escapechar='"',   \
                 parse_dates=[0, 2]).rename(columns=lambda x: x.split("=")[0])
df

df.dtypes

XAU    datetime64[ns]
XAU           float64
XAG    datetime64[ns]
XAG           float64
dtype: object

df = pd.read_csv(data, quoting=csv.QUOTE_NONE, index_col=False, escapechar='"',   \
                 parse_dates=[0], usecols=[0,1]).rename(columns=lambda x: x.split("=")[0])

df

df.columns = df.columns + [str('_%d'%(i)) for i in list(range(len(df.columns)))]

ser = pd.Series(data=df['XAU_1'].values, index=df['XAU_0'].values, name='XAU')
ser

2014-08-25 06:00:05    1200.343
2014-08-25 06:00:05    1200.000
Name: XAU, dtype: float64

type(ser)
pandas.core.series.Series

data = StringIO(
'''
"XAU=,XAU=,XAG=,XAG="   
"25/08/2014 6:00:05,1200.343,25/08/2014 6:00:03,19.44," 
"25/08/2014 6:00:05,1200,,,"
",,25/08/2014 6:00:05,19.50,"       
''')


df = pd.read_csv(data, quoting=csv.QUOTE_NONE, index_col=False, na_values=[""], 
                 parse_dates=[2]).rename(columns=lambda x: x.strip('"').split("=")[0])

old_cols = df.columns
# Index(['XAU', 'XAU', 'XAG', 'XAG'], dtype='object')

new_cols = [col[0] for col in list(enumerate(df.columns))]
# [0, 1, 2, 3]
df.columns = new_cols

# Converting first column to datetime dtype
df[0] = pd.to_datetime(df[0].str.replace('"', ''))   
df.columns = old_cols

df

df.dtypes

XAU    datetime64[ns]
XAU           float64
XAG    datetime64[ns]
XAG           float64
dtype: object

In [11]: df["count"] = df.groupby("label").cumcount()

In [12]: df
Out[12]:
  label  value  count
0     a    0.2      0
1     a    0.1      1
2     a    0.4      2
3     b    0.5      0
4     b    0.2      1
5     b    0.6      2
6     c    0.7      0
7     c    0.9      1
8     c    0.3      2

In [13]: df.pivot_table("value", "count", "label")
Out[13]:
label    a    b    c
count
0      0.2  0.5  0.7
1      0.1  0.2  0.9
2      0.4  0.6  0.3

In [21]: df["value"].values.reshape((-1, 3)).T
Out[21]:
array([[ 0.2,  0.5,  0.7],
       [ 0.1,  0.2,  0.9],
       [ 0.4,  0.6,  0.3]])

In [22]: pd.DataFrame(df["value"].values.reshape((-1, 3)).T, 
                      columns=df.loc[::3, "label"])
Out[22]:
label    a    b    c
0      0.2  0.5  0.7
1      0.1  0.2  0.9
2      0.4  0.6  0.3

c = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

df = pd.pivot(index=pd.CategoricalIndex(df.index.strftime('%b'), ordered=True, categories=c),
              columns=df.index.year,
              values=df['value'])
print (df)

       2015    2016    2017
Jan  201.55  201.65  201.75
Feb  201.60  201.70  201.80
Mar  201.65  201.75  201.85
Apr  201.70  201.80  201.90
May  201.75  201.85  201.95
Jun  201.80  201.90  202.00
Jul  201.85  201.95  202.05
Aug  201.90  202.00  202.10
Sep  201.95  202.05  202.15
Oct  202.00  202.10  202.20
Nov  202.05  202.15  202.25
Dec  202.10  202.20  202.30

df.plot()

df['months'] = pd.CategoricalIndex(df.index.strftime('%b'), ordered=True, categories=c)
df['years'] = df.index.year
df = df.pivot(index='months', columns='years',values='value')

import urllib
import pandas as pd

df = pd.DataFrame()
urls = ['https://www.google.com/something','https://mail.google.com/anohtersomething', 'https://www.amazon.com/yetanotherthing']
df['protocol'],df['domain'],df['path'],df['query'],df['fragment'] = zip(*[urllib.parse.urlsplit(x) for x in urls])

  protocol           domain               path query fragment
0    https   www.google.com         /something
1    https  mail.google.com  /anohtersomething
2    https   www.amazon.com   /yetanotherthing

for c in df.columns:
    if c.endswith('_date'):
        parse_column(df, c)

for c in my_columns_list:
    parse_column(df, c)