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Licensed under CC BY 4.0 Creative Commons
If you want to follow along, this is a notebook that you can view or run yourself:
pandas >= 0.15.2 (easy solution is using Anaconda)Some imports:
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%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn
pd.options.display.max_rows = 8
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data = pd.read_csv('data/airbase_data.csv', index_col=0, parse_dates=True)
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data
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to answering questions about this data in a few lines of code:
Does the air pollution show a decreasing trend over the years?
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data['1999':].resample('A').plot(ylim=[0,100])
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How many exceedances of the limit values?
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exceedances = data > 200
exceedances = exceedances.groupby(exceedances.index.year).sum()
ax = exceedances.loc[2005:].plot(kind='bar')
ax.axhline(18, color='k', linestyle='--')
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What is the difference in diurnal profile between weekdays and weekend?
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data['weekday'] = data.index.weekday
data['weekend'] = data['weekday'].isin([5, 6])
data_weekend = data.groupby(['weekend', data.index.hour])['FR04012'].mean().unstack(level=0)
data_weekend.plot()
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We will come back to these example, and build them up step by step.
For data-intensive work in Python the Pandas library has become essential.
What is pandas?
R's data.frame in Python.It's documentation: http://pandas.pydata.org/pandas-docs/stable/
.dropna(), pd.isnull())concat, join)groupby functionalitystack, pivot)Some recent enhancements of the last year (versions 0.14 to 0.16):
Categorical and CategoricalIndex)Timedelta and TimedeltaIndexsqlalchemy.dt accessor for accesing datetime-properties from columnsSlides and data: Source: https://github.com/jorisvandenbossche/2015-EuroScipy-pandas-tutorial
Slides presented with 'live reveal' https://github.com/damianavila/RISE
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