notebook.community

Edit and run



In [1]:

    
import pandas as pd
import numpy as np
import matplotlib as mpl

import matplotlib.pyplot as plt

from datetime import date



In [2]:

    
pd.__version__,np.__version__, mpl.__version__









    Out[2]:





('0.24.2', '1.16.3', '3.0.3')



In [3]:

    
df = pd.DataFrame({
    'name':[
        'john','mary','peter','jeff','bill'
    ],
    'date_of_birth':[
        '2000-01-01', '1999-12-20', '2000-11-01', '1995-02-25', '1992-06-30',
    ],
})
df









    Out[3]:







  
    
      
      name
      date_of_birth
    
  
  
    
      0
      john
      2000-01-01
    
    
      1
      mary
      1999-12-20
    
    
      2
      peter
      2000-11-01
    
    
      3
      jeff
      1995-02-25
    
    
      4
      bill
      1992-06-30



In [4]:

    
df.index









    Out[4]:





RangeIndex(start=0, stop=5, step=1)

use date column as index



In [5]:

    
datetime_series = pd.to_datetime(df['date_of_birth'])

datetime_index = pd.DatetimeIndex(datetime_series.values)

df2=df.set_index(datetime_index)

df2.drop('date_of_birth',axis=1,inplace=True)

df2



In [6]:

    
df2.index









    Out[6]:





DatetimeIndex(['2000-01-01', '1999-12-20', '2000-11-01', '1995-02-25',
               '1992-06-30'],
              dtype='datetime64[ns]', freq=None)

fill empty periods



In [7]:

    
df = pd.DataFrame({
    'name':[
        'john','mary','peter','jeff','bill'
    ],
    'year_born':[
        '2000', '1999', '2001', '1995', '1992',
    ],
})
df



In [8]:

    
df.index









    Out[8]:





RangeIndex(start=0, stop=5, step=1)



In [9]:

    
datetime_series = pd.to_datetime(df['year_born'])

datetime_index = pd.DatetimeIndex(datetime_series.values)

df3=df.set_index(datetime_index)

df3.drop('year_born',axis=1,inplace=True)

df3.sort_index(inplace=True)

df3



In [10]:

    
df3.index









    Out[10]:





DatetimeIndex(['1992-01-01', '1995-01-01', '1999-01-01', '2000-01-01',
               '2001-01-01'],
              dtype='datetime64[ns]', freq=None)



In [11]:

    
df4=df3.asfreq('YS')
df4



In [12]:

    
df4.index









    Out[12]:





DatetimeIndex(['1992-01-01', '1993-01-01', '1994-01-01', '1995-01-01',
               '1996-01-01', '1997-01-01', '1998-01-01', '1999-01-01',
               '2000-01-01', '2001-01-01'],
              dtype='datetime64[ns]', freq='AS-JAN')

lag columns



In [13]:

    
df = pd.DataFrame(
    data={'reading': np.random.uniform(high=100,size=10)},
    index=pd.to_datetime([date(2019,1,d) for d in range(1,11)])
)
df



In [14]:

    
df['reading_d_minus_1']=df['reading'].shift(1,freq='D')

df['reading_d_minus_2']=df['reading'].shift(2,freq='D')



In [15]:

    
df









    Out[15]:







  
    
      
      reading
      reading_d_minus_1
      reading_d_minus_2
    
  
  
    
      2019-01-01
      58.720972
      NaN
      NaN
    
    
      2019-01-02
      3.101846
      58.720972
      NaN
    
    
      2019-01-03
      84.729331
      3.101846
      58.720972
    
    
      2019-01-04
      67.595894
      84.729331
      3.101846
    
    
      2019-01-05
      82.898735
      67.595894
      84.729331
    
    
      2019-01-06
      59.234635
      82.898735
      67.595894
    
    
      2019-01-07
      43.735153
      59.234635
      82.898735
    
    
      2019-01-08
      16.699276
      43.735153
      59.234635
    
    
      2019-01-09
      31.154091
      16.699276
      43.735153
    
    
      2019-01-10
      45.747997
      31.154091
      16.699276

plot data aggregated by day



In [17]:

    
from datetime import date,datetime,timedelta

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd

np.random.seed(42)

# create a dummy dataset
df = pd.DataFrame(
    index=pd.to_datetime(
        [datetime(2019,1,1)+timedelta(hours=h+10*np.random.uniform()) for h in range(0,1000)]
    ),
    data={
        'browser': np.random.choice([
            'chrome',
            'firefox',
            'safari',
            'edge'
        ],size=1000,replace=True)
    },
    
)

print(type(df.index))
# 'pandas.core.indexes.datetimes.DatetimeIndex'

# just to make it more intuitive
df.index.name= 'login_datetime'

df.groupby(by=[lambda dt: dt.strftime('%Y-%m-%d'),'browser']).size().groupby(level=0).apply(
    lambda x: 100 * x / x.sum()
).unstack().plot(kind='bar',stacked=True,legend='reverse',width=0.8)

current_handles, current_labels = plt.gca().get_legend_handles_labels()

# sort or reorder the labels and handles
reversed_handles = list(reversed(current_handles))
reversed_labels = list(reversed(current_labels))

# call plt.legend() with the new values
plt.legend(reversed_handles,reversed_labels,loc='center left', bbox_to_anchor=(1, 0.5))
plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.gcf().set_size_inches(12,5)
plt.title('Agreggated number of visits to website, per day and browser')
plt.show()









    



<class 'pandas.core.indexes.datetimes.DatetimeIndex'>



In [ ]:

	name	date_of_birth
0	john	2000-01-01
1	mary	1999-12-20
2	peter	2000-11-01
3	jeff	1995-02-25
4	bill	1992-06-30

	name
1992-01-01	bill
1995-01-01	jeff
1999-01-01	mary
2000-01-01	john
2001-01-01	peter

	name
1992-01-01	bill
1993-01-01	NaN
1994-01-01	NaN
1995-01-01	jeff
1996-01-01	NaN
1997-01-01	NaN
1998-01-01	NaN
1999-01-01	mary
2000-01-01	john
2001-01-01	peter

	reading
2019-01-01	58.720972
2019-01-02	3.101846
2019-01-03	84.729331
2019-01-04	67.595894
2019-01-05	82.898735
2019-01-06	59.234635
2019-01-07	43.735153
2019-01-08	16.699276
2019-01-09	31.154091
2019-01-10	45.747997