How to utilise timeseries in pandas?
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How to utilise timeseries in pandas?

How to utilise timeseries in pandas?

This recipe helps you utilise timeseries in pandas

0
This data science python source code does the following: 1. Creating your own data dictionary and converting it to dataframe. 2. Timestamping of the categorical features(Dates). 3. Loads the timestamped data and performings basic EDA.
In [2]:
## How to utilise timeseries in pandas
def Snippet_112():
    print()
    print(format('How to utilise timeseries in pandas','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from datetime import datetime
    import pandas as pd

    # %matplotlib inline
    import matplotlib.pyplot as pyplot

    # Create a dataframe
    data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994',
                     '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071',
                     '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592',
                     '2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109',
                     '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'],
            'car_sales': [34, 25, 26, 15, 15, 14, 26, 25, 62, 41]}
    df = pd.DataFrame(data, columns = ['date', 'car_sales'])
    print(df)

    # Convert df['date'] from string to datetime
    df['date'] = pd.to_datetime(df['date'])

    # Set df['date'] as the index and delete the column
    df.index = df['date']
    del df['date']
    print(); print(df)

    # View all observations that occured in 2014
    print(); print(df['2014'])

    # View all observations that occured in May 2014
    print(); print(df['2014-05'])

    # Observations after May 3rd, 2014
    print(); print(df[datetime(2014, 5, 3):])

    # Observations between May 3rd and May 4th
    print(); print(df['5/3/2014':'5/4/2014'])

    # Truncation observations after May 2nd 2014
    print(); print(df.truncate(after='5/3/2014'))

    # Observations of May 2014
    print(); print(df['5-2014'])

    # Count the number of observations per timestamp
    print(); print(df.groupby(level=0).count())

    # Mean value of car_sales per day
    print(); print(df.resample('D').mean())

    # Total value of car_sales per day
    print(); print(df.resample('D').sum())

    # Plot of the total car_sales per day
    df.resample('D').sum().plot(); pyplot.show()

Snippet_112()
***********************How to utilise timeseries in pandas************************
                         date  car_sales
0  2014-05-01 18:47:05.069722         34
1  2014-05-01 18:47:05.119994         25
2  2014-05-02 18:47:05.178768         26
3  2014-05-02 18:47:05.230071         15
4  2014-05-02 18:47:05.230071         15
5  2014-05-02 18:47:05.280592         14
6  2014-05-03 18:47:05.332662         26
7  2014-05-03 18:47:05.385109         25
8  2014-05-04 18:47:05.436523         62
9  2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-01 18:47:05.069722         34
2014-05-01 18:47:05.119994         25
2014-05-02 18:47:05.178768         26
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.280592         14
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-01 18:47:05.069722         34
2014-05-01 18:47:05.119994         25
2014-05-02 18:47:05.178768         26
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.280592         14
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-01 18:47:05.069722         34
2014-05-01 18:47:05.119994         25
2014-05-02 18:47:05.178768         26
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.280592         14
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-01 18:47:05.069722         34
2014-05-01 18:47:05.119994         25
2014-05-02 18:47:05.178768         26
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.280592         14

                            car_sales
date
2014-05-01 18:47:05.069722         34
2014-05-01 18:47:05.119994         25
2014-05-02 18:47:05.178768         26
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.230071         15
2014-05-02 18:47:05.280592         14
2014-05-03 18:47:05.332662         26
2014-05-03 18:47:05.385109         25
2014-05-04 18:47:05.436523         62
2014-05-04 18:47:05.486877         41

                            car_sales
date
2014-05-01 18:47:05.069722          1
2014-05-01 18:47:05.119994          1
2014-05-02 18:47:05.178768          1
2014-05-02 18:47:05.230071          2
2014-05-02 18:47:05.280592          1
2014-05-03 18:47:05.332662          1
2014-05-03 18:47:05.385109          1
2014-05-04 18:47:05.436523          1
2014-05-04 18:47:05.486877          1

            car_sales
date
2014-05-01       29.5
2014-05-02       17.5
2014-05-03       25.5
2014-05-04       51.5

            car_sales
date
2014-05-01         59
2014-05-02         70
2014-05-03         51
2014-05-04        103
In [ ]:

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