Pandas Time Series Basics
Import modules
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'],
'battle_deaths': [34, 25, 26, 15, 15, 14, 26, 25, 62, 41]}
df = pd.DataFrame(data, columns = ['date', 'battle_deaths'])
print(df)
date battle_deaths
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
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']
df
battle_deaths | |
---|---|
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 |
View all observations that occured in 2014
df['2014']
battle_deaths | |
---|---|
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 |
View all observations that occured in May 2014
df['2014-05']
battle_deaths | |
---|---|
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 |
Observations after May 3rd, 2014
df[datetime(2014, 5, 3):]
battle_deaths | |
---|---|
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 |
Observations between May 3rd and May 4th
df['5/3/2014':'5/4/2014']
battle_deaths | |
---|---|
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 |
Truncation observations after May 2nd 2014
df.truncate(after='5/3/2014')
battle_deaths | |
---|---|
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 |
Observations of May 2014
df['5-2014']
battle_deaths | |
---|---|
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 |
Count the number of observations per timestamp
df.groupby(level=0).count()
battle_deaths | |
---|---|
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 |
Mean value of battle_deaths per day
df.resample('D').mean()
battle_deaths | |
---|---|
date | |
2014-05-01 | 29.5 |
2014-05-02 | 17.5 |
2014-05-03 | 25.5 |
2014-05-04 | 51.5 |
Total value of battle_deaths per day
df.resample('D').sum()
battle_deaths | |
---|---|
date | |
2014-05-01 | 59 |
2014-05-02 | 70 |
2014-05-03 | 51 |
2014-05-04 | 103 |
Plot of the total battle deaths per day
df.resample('D').sum().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11187a940>
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