import pandas as pd
import numpy as np
data = pd.read_csv('../data/macrodata.csv')
data.head()
0 1959.0
1 1959.0
2 1959.0
3 1959.0
4 1960.0
...
198 2008.0
199 2008.0
200 2009.0
201 2009.0
202 2009.0
Name: year, Length: 203, dtype: float64
0 1.0
1 2.0
2 3.0
3 4.0
4 1.0
...
198 3.0
199 4.0
200 1.0
201 2.0
202 3.0
Name: quarter, Length: 203, dtype: float64
inflindex = pd.PeriodIndex(year = data.year, quarter=data.quarter, freq='Q-DEC')
inflindex
PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',
'1960Q3', '1960Q4', '1961Q1', '1961Q2',
...
'2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',
'2008Q4', '2009Q1', '2009Q2', '2009Q3'],
dtype='period[Q-DEC]', length=203, freq='Q-DEC')
data.index = inflindex
data.infl
1959Q1 0.00
1959Q2 2.34
1959Q3 2.74
1959Q4 0.27
1960Q1 2.31
...
2008Q3 -3.16
2008Q4 -8.79
2009Q1 0.94
2009Q2 3.37
2009Q3 3.56
Freq: Q-DEC, Name: infl, Length: 203, dtype: float64
1959Q1 177.146
1959Q2 177.830
1959Q3 178.657
1959Q4 179.386
1960Q1 180.007
...
2008Q3 305.270
2008Q4 305.952
2009Q1 306.547
2009Q2 307.226
2009Q3 308.013
Freq: Q-DEC, Name: pop, Length: 203, dtype: float64
%matplotlib notebook
data.infl.plot()
rng = pd.date_range('2019-09-01', periods=110, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
2019-09-01 0.313532
2019-09-02 -0.022779
2019-09-03 -1.431959
2019-09-04 -1.628216
2019-09-05 -0.263348
...
2019-12-15 1.949090
2019-12-16 0.039795
2019-12-17 -0.425080
2019-12-18 -1.332584
2019-12-19 -1.845546
Freq: D, Length: 110, dtype: float64
ts.resample('M').mean()
2019-09-30 -0.413720
2019-10-31 -0.040300
2019-11-30 -0.004320
2019-12-31 0.269151
Freq: M, dtype: float64
ts.resample('M', kind='period').min()
2019-09 -3.115553
2019-10 -2.315550
2019-11 -1.479132
2019-12 -1.845546
Freq: M, dtype: float64
rng = pd.date_range('2019-01-01', periods=12, freq='T')
ts = pd.Series(np.arange(len(rng)), index=rng)
ts
2019-01-01 00:00:00 0
2019-01-01 00:01:00 1
2019-01-01 00:02:00 2
2019-01-01 00:03:00 3
2019-01-01 00:04:00 4
2019-01-01 00:05:00 5
2019-01-01 00:06:00 6
2019-01-01 00:07:00 7
2019-01-01 00:08:00 8
2019-01-01 00:09:00 9
2019-01-01 00:10:00 10
2019-01-01 00:11:00 11
Freq: T, dtype: int64
ts.resample('5min').sum()
2019-01-01 00:00:00 10
2019-01-01 00:05:00 35
2019-01-01 00:10:00 21
Freq: 5T, dtype: int64
import os
import json
from FrozenJSON import FrozenJSON
import glob
import pandas as pd
def loads(JSON):
with open(JSON) as fp:
return json.load(fp)
def get_data():
files = [os.path.basename(x) for x in glob.glob("../data/weather/*")]
data = {}
for file in files:
feed = FrozenJSON(loads('../data/weather/'+file))
temp = float(feed.main.temp) - 273.15
ctime = file[12:-5]
data[ctime] = '{: .2f}'.format(temp)
return data
def format_data(data):
df = pd.DataFrame.from_dict(data, orient='index')
df = df.sort_index().reset_index()
df.columns = ['dt', 'temp']
df['dt'] = pd.to_datetime(df['dt'])
df['temp'] = pd.to_numeric(df['temp'])
return df
data = get_data()
df = format_data(data)
df.head(100)
61 rows × 2 columns
%matplotlib notebook
df.index = df.dt
df.temp.plot()
df = df.resample("T").ffill()
%matplotlib notebook
df.temp.plot()
%matplotlib notebook
df.temp.plot()
df.temp.rolling(window = 110).mean().plot(label = 'moving average')
df.temp.ewm(span = 10).mean().plot(label = 'WE MA').legend()
