<jupyter_start><jupyter_text>Calendar Plot> Sometimes it is useful to visualise data in a familiar way. Calendars are the obvious wayto represent data for data on the time scale of days or months. The calendarPlotfunction provides an effective way to visualise data in this way by showing dailyconcentrations laid out in a calendar format. The concentration of a species is shownby its colour.  Standard libraries to be imported for usage<jupyter_code>import pandas as pd
import numpy as np
from vayu import calendarPlot<jupyter_output><empty_output><jupyter_text>Using `mydata.csv`Reading data.<jupyter_code>df = pd.read_csv('../data/mydata.csv')
df.head()<jupyter_output><empty_output><jupyter_text>Converting `date` column to be of the type `datetime64`. And taking daily averages.<jupyter_code>df['date']= pd.to_datetime(df['date'])
df = df.groupby([df['date'].dt.date]).mean()
df.head()<jupyter_output><empty_output><jupyter_text>Resetting index<jupyter_code>df = df.reset_index()
df.head()<jupyter_output><empty_output><jupyter_text>Getting a date column   **Note**: Please note the datatypes of the different columns.<jupyter_code>calendarPlot(df, 'pm25', '2003', 'India')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10339123242349049, 'fraction_numerical': 0.006617038875103391, 'mean_word_length': 7.287671232876712, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 22, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '654200', 'license': 'MIT', 'n_tokens_mistral': 396, 'n_tokens_neox': 381, 'n_words': 116, 'path': 'docs/examples/nbs/calendarPlot.ipynb', 'repo_name': 'tanmay-1srivastava/vayu', 'size': '1209'}