Calendar Plot

Sometimes it is useful to visualize data in a familiar way. Calendars are the obvious way to represent data for data on the time scale of days or months. The calendarPlot function provides an effective way to visualize data in this way by showing daily concentrations laid out in a calendar format. The concentration of a species is shown by its color.

import pandas as pd
import numpy as np
from vayu import calendarPlot

Reading data.

df = pd.read_csv('data/mydata.csv.tar.gz')
df.head()

	date	ws	wd	nox	no2	o3	pm10	so2	co	pm25
0	01/01/1998 00:00	0.60	280.0	285.0	39.0	1.0	29.0	4.7225	3.3725	NaN
1	01/01/1998 01:00	2.16	230.0	NaN	NaN	NaN	37.0	NaN	NaN	NaN
2	01/01/1998 02:00	2.76	190.0	NaN	NaN	3.0	34.0	6.8300	9.6025	NaN
3	01/01/1998 03:00	2.16	170.0	493.0	52.0	3.0	35.0	7.6625	10.2175	NaN
4	01/01/1998 04:00	2.40	180.0	468.0	78.0	2.0	34.0	8.0700	8.9125	NaN

Converting date column to be of the type datetime64. And taking daily averages.

df['date']= pd.to_datetime(df['date'])
daily_df = df.groupby([df['date'].dt.date]).mean(numeric_only=True)
daily_df = daily_df.reset_index()
daily_df.head()

	date	index	ws	wd	nox	no2	o3	pm10	so2	co	pm25
0	1998-01-01	11.5	6.835	191.666667	153.954545	39.363636	6.869565	18.166667	3.152609	2.699239	NaN
1	1998-01-02	755.5	1.175	89.166667	149.375000	41.208333	2.250000	25.000000	4.862882	1.857882	NaN
2	1998-01-03	1427.5	6.750	232.500000	250.500000	51.500000	3.375000	23.416667	9.811528	2.984965	NaN
3	1998-01-04	2171.5	2.215	98.333333	239.541667	46.541667	1.875000	45.583333	21.792065	2.409271	NaN
4	1998-01-05	2891.5	3.570	325.416667	79.625000	34.333333	17.625000	23.000000	2.103958	0.930347	16.235294

Getting a date column

Note: Please note the datatypes of the different columns.

calendarPlot(df, pollutant='pm25', year='2003', country='India');

/home/patel_zeel/miniconda3/lib/python3.9/site-packages/vayu/calendarPlot.py:99: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
  df_year = df[year].resample("1D").mean()