/ TIMESERIES

Time Series Analysis - Time Series with Pandas

Time Series with Pandas

from datetime import datetime

my_year = 2020
my_month = 1
my_day = 2
my_hour = 13
my_min = 30
my_sec = 15

my_date = datetime(my_year, my_month, my_day)

my_date

datetime.datetime(2020, 1, 2, 0, 0)

my_date_time = datetime(my_year, my_month, my_day, my_hour, my_min,my_sec)

my_date_time

datetime.datetime(2020, 1, 2, 13, 30, 15)

my_date_time.day

2

import numpy as np

np.array(['2020-03-15','2020-03-16','2020-03-17'], dtype = 'datetime64[D]')

array(['2020-03-15', '2020-03-16', '2020-03-17'], dtype='datetime64[D]')

np.arange('2018-06-01','2018-07-23',7, dtype = 'datetime64[D]')

array(['2018-06-01', '2018-06-08', '2018-06-15', '2018-06-22',
       '2018-06-29', '2018-07-06', '2018-07-13', '2018-07-20'],
      dtype='datetime64[D]')

import pandas as pd

pd.date_range('2020-01-01',periods=10, freq='D')

DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
               '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
               '2020-01-09', '2020-01-10'],
              dtype='datetime64[ns]', freq='D')

pd.date_range('Jan 01, 2018',periods=10, freq='D')

DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
               '2018-01-09', '2018-01-10'],
              dtype='datetime64[ns]', freq='D')

pd.to_datetime(['1/2/2018', 'Jan 03, 2018'])

DatetimeIndex(['2018-01-02', '2018-01-03'], dtype='datetime64[ns]', freq=None)

pd.to_datetime(['1/2/2018', '3/1/2018'], format='%d/%m/%Y')

DatetimeIndex(['2018-02-01', '2018-01-03'], dtype='datetime64[ns]', freq=None)

pd.to_datetime(['1--2--2018', '3--1--2018'], format='%d--%m--%Y')

DatetimeIndex(['2018-02-01', '2018-01-03'], dtype='datetime64[ns]', freq=None)

pd.to_datetime(['1#2#2018', '3#1#2018'], format='%d#%m#%Y')

DatetimeIndex(['2018-02-01', '2018-01-03'], dtype='datetime64[ns]', freq=None)

data = np.random.randn(3,2)
cols = ['A','B']
print(data)

[[-1.13254154  1.48492529]
 [ 1.58659165  0.12557822]
 [ 0.47964139  0.28469417]]

idx = pd.date_range('2020-01-01', periods=3, freq='D')

df = pd.DataFrame(data, columns = cols, index = idx)

df
A B
2020-01-01 -1.132542 1.484925
2020-01-02 1.586592 0.125578
2020-01-03 0.479641 0.284694 
df.index

DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03'], dtype='datetime64[ns]', freq='D')

df.index.max()

Timestamp('2020-01-03 00:00:00', freq='D')

df.index.argmax()

2

df.index.min()

Timestamp('2020-01-01 00:00:00', freq='D')

df.index.argmin()

0

Time Resampling

df = pd.read_csv('Data/starbucks.csv',index_col='Date',parse_dates=True)

df.index

DatetimeIndex(['2015-01-02', '2015-01-05', '2015-01-06', '2015-01-07',
               '2015-01-08', '2015-01-09', '2015-01-12', '2015-01-13',
               '2015-01-14', '2015-01-15',
               ...
               '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',
               '2018-12-21', '2018-12-24', '2018-12-26', '2018-12-27',
               '2018-12-28', '2018-12-31'],
              dtype='datetime64[ns]', name='Date', length=1006, freq=None)

# daily to yearly
df.resample(rule = 'A').mean()
Close Volume
Date
2015-12-31 50.078100 8.649190e+06
2016-12-31 53.891732 9.300633e+06
2017-12-31 55.457310 9.296078e+06
2018-12-31 56.870005 1.122883e+07 
def first_day(entry):
    
    # IS THERE AN ENTRY??
    if len(entry):
        # IF SO, RETURN FIRST ENTRY
        return entry[0]

df.resample(rule='A').apply(first_day)
Close Volume
Date
2015-12-31 38.0061 6906098
2016-12-31 55.0780 13521544
2017-12-31 53.1100 7809307
2018-12-31 56.3243 7215978 
df['Close'].resample('A').mean().plot.bar()

<AxesSubplot:xlabel='Date'>

png

Time Shifting

df
Close Volume
Date
2015-01-02 38.0061 6906098
2015-01-05 37.2781 11623796
2015-01-06 36.9748 7664340
2015-01-07 37.8848 9732554
2015-01-08 38.4961 13170548
... ... ...
2018-12-24 60.5600 6323252
2018-12-26 63.0800 16646238
2018-12-27 63.2000 11308081
2018-12-28 63.3900 7712127
2018-12-31 64.4000 7690183

1006 rows × 2 columns

df.tail()
Close Volume
Date
2018-12-24 60.56 6323252
2018-12-26 63.08 16646238
2018-12-27 63.20 11308081
2018-12-28 63.39 7712127
2018-12-31 64.40 7690183 
df.shift(1).tail()
Close Volume
Date
2018-12-24 61.39 23524888.0
2018-12-26 60.56 6323252.0
2018-12-27 63.08 16646238.0
2018-12-28 63.20 11308081.0
2018-12-31 63.39 7712127.0 
df.shift(periods=1, freq='M').head()
Close Volume
Date
2015-01-31 38.0061 6906098
2015-01-31 37.2781 11623796
2015-01-31 36.9748 7664340
2015-01-31 37.8848 9732554
2015-01-31 38.4961 13170548 
df.head()
Close Volume
Date
2015-01-02 38.0061 6906098
2015-01-05 37.2781 11623796
2015-01-06 36.9748 7664340
2015-01-07 37.8848 9732554
2015-01-08 38.4961 13170548

Rolling and Expanding

df['Close'].plot(figsize=(12,8))

<AxesSubplot:xlabel='Date'>

png

df['Close'].plot(figsize=(12,5), legend=True)
df.rolling(window=30).mean()['Close'].plot(legend=True)

<AxesSubplot:xlabel='Date'>

png

df['Close: 30 Day Mean'] = df['Close'].rolling(window=30).mean()

df.head()
Close Volume Close: 30 Day Mean
Date
2015-01-02 38.0061 6906098 NaN
2015-01-05 37.2781 11623796 NaN
2015-01-06 36.9748 7664340 NaN
2015-01-07 37.8848 9732554 NaN
2015-01-08 38.4961 13170548 NaN 
df[['Close', 'Close: 30 Day Mean']].plot(figsize=(12,5))

<AxesSubplot:xlabel='Date'>

png

df['Close'].expanding().mean().plot(figsize=(12,5))

<AxesSubplot:xlabel='Date'>

png

Visualizing Time Series Data with Pandas

df.index = pd.to_datetime(df.index)

df.index

DatetimeIndex(['2015-01-02', '2015-01-05', '2015-01-06', '2015-01-07',
               '2015-01-08', '2015-01-09', '2015-01-12', '2015-01-13',
               '2015-01-14', '2015-01-15',
               ...
               '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',
               '2018-12-21', '2018-12-24', '2018-12-26', '2018-12-27',
               '2018-12-28', '2018-12-31'],
              dtype='datetime64[ns]', name='Date', length=1006, freq=None)

df.columns

Index(['Close', 'Volume', 'Close: 30 Day Mean'], dtype='object')

df.drop(['Close: 30 Day Mean'], axis=1, inplace=True)

df.plot()

<AxesSubplot:xlabel='Date'>

png

df['Close'].plot()

<AxesSubplot:xlabel='Date'>

png

title = 'TITLE'
ylabel = 'Y label'
xlabel = 'X label'

ax = df['Close'].plot(figsize=(12,5), title=title, color='wheat')
ax.autoscale(axis='both', tight = True)
ax.set(xlabel=xlabel, ylabel=ylabel)

[Text(0.5, 0, 'X label'), Text(0, 0.5, 'Y label')]

png

df['Close']['2017-01-01':'2017-12-31'].plot(figsize=(12,4))

<AxesSubplot:xlabel='Date'>

png

df['Close'].plot(figsize=(12,4), xlim=['2017-01-01','2017-12-31'], ylim=[50,65]
                , ls='--', c='red')

<AxesSubplot:xlabel='Date'>

png

from matplotlib import dates

ax = df['Close'].plot(xlim=['2017-01-01','2017-03-01'], ylim=[50,60],figsize=(12,5))
ax.set(xlabel='xlabel')

[Text(0.5, 0, 'xlabel')]

png

ax = df['Close'].plot(xlim=['2017-01-01','2017-03-01'], ylim=[50,60],figsize=(12,5))
ax.set(xlabel='xlabel')
ax.xaxis.set_major_locator(dates.WeekdayLocator(byweekday=0))
ax.xaxis.set_major_formatter(dates.DateFormatter('%a-%B-%d'))

png

ax = df['Close'].plot(xlim=['2017-01-01','2017-03-01'], ylim=[50,60],figsize=(12,5))
ax.set(xlabel='xlabel')
ax.xaxis.set_major_locator(dates.WeekdayLocator(byweekday=0))
ax.xaxis.set_major_formatter(dates.DateFormatter('%d'))

ax.xaxis.set_minor_locator(dates.MonthLocator())
ax.xaxis.set_minor_formatter(dates.DateFormatter('\n\n%b'))

ax.yaxis.grid(True)
ax.xaxis.grid(True)

png