Introduction to Pandas and Time Series Analysis [PyCon DE]

Introduction to Pandas
(and Time Series Analysis)
Alexander C. S. Hendorf
@hendorf
Best-of Version

Königsweg GmbH
Königsweg affiliate high-tech startups and the industry
EuroPython Organisator + Programm Chair
mongoDB master 2016, MUG Leader
Speaker mongoDB days, EuroPython, PyData…
@hendorf

Origin und Goals
-Open Source Python Library
-practical real-world data analysis - fast, efficient & easy
-gapless workflow (no switching to e.g. R language)
-2008 started by Wes McKinney,  
now PyData stack at Continuum Analytics ("Anaconda")
-very stable project with regular updates
-https://github.com/pydata/pandas

Main Features
-Support for CSV, Excel, JSON, SQL, SAS, clipboard, HDF5,…
-Data cleansing
-Re-shape & merge data (joins & merge) & pivoting
-Data Visualisation
-Well integrated in Jupyter (iPython) notebooks
-Database-like operations
-Performant

Today
Part 1:
Basic functionality of Pandas
Part 2:
A deeper look at the index with the TimeSeries Index
Git featuring this presentation's code examples:
https://github.com/Koenigsweg/data-timeseries-analysis-with-pandas

2014-08-21T22:50:00,12.0
2014-08-17T13:20:00,16.0
2014-08-06T01:20:00,14.0
2014-09-27T06:50:00,11.0
2014-08-25T21:50:00,13.0
2014-08-14T05:20:00,13.0
2014-09-14T05:20:00,16.0
2014-08-03T02:50:00,21.0
2014-09-29T03:00:00,13
2014-09-06T08:20:00,16.0
2014-08-19T07:20:00,13.0
2014-09-27T22:50:00,10.0
2014-08-28T08:20:00,12.0
2014-08-17T01:00:00,14
2014-09-27T14:00:00,17
2014-09-10T18:00:00,18
2014-09-22T23:00:00,8
2014-09-20T03:00:00,9
2014-08-29T09:50:00,16.0
2014-08-16T01:50:00,13.0
2014-08-28T22:00:00,14

I/O and viewing data
-convention import pandas as pd
-example pd.read_csv()
-very flexible, ~40 optional parameters included (delimiter,
header, dtype, parse_dates,…)
-preview data with .head(#number of lines) and .tail(#)

ax = df[:100].plot()
ax.axhline(16, color='r', linestyle='-')
df.plot(kind='bar')

Visualisation
-matplotlib (http://matplotlib.org) integrated, .plot()
-custom- and extendable, plot() returns ax
-Bar-, Area-, Scatter-, Boxplots u.a.
-Alternatives:  
Bokeh (http://bokeh.pydata.org/en/latest/) 
Seaborn (https://stanford.edu/~mwaskom/software/seaborn/index.html)

Structure
pd.Series
Index
pd.DataFrame
Data
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Structure: DataSeries
-one dimensional, labeled series, may contain any data type
-the label of the series is usually called index
-index automatically created if not given
-One data type,  
datatype can be set or transformed dynamically in a pythonic fashion 
e. g. explicitly set

simple series, auto data type auto, index auto
simple series, auto data type auto, index auto
simple series, auto data type set, index auto

simple series, auto data type set, numerical index given
simple series, auto data type set, text-label index given

access via index / label
access via index / position
access multiple via index / label
access multiple via index / position range
access multiple via index / multiple positions
access via boolean index / lambda function

.loc()
index label
.iloc()
index position
.ix()
index guessing
label/position fallback

.name
(column) names
.sample()
sampling data set

Selecting Data
-Slicing
-Boolean indexing
series[x], series[[x, y]]
series[2], series[[2, 3]], series[2:3]
series.ix() / .iloc() / .loc()
series.sample()

Structure: DataFrame
-Twodimensional, labeled data structure of e. g.
-DataSeries
-2-D numpy.ndarray
-other DataFrames
-index automatically created if not given

Structure: Index
-Index
-automatically created if not given
-can be reset or replaced
-types: position, timestamp, time range, labels,…
-one or more dimensions
-may contain a value more than once (NOT UNIQUE!)

Examples
-work with series / calculation
-create and add a new series
-how to deal with null (NaN) values
-method calls directly from Series/ DataFrames

Modifying Series/DataFrames
-Methods applied to Series or DataFrames do not change them, but 
return the result as Series or DataFrames
-With parameter inplace the result can be deployed directly into Series /
DataFrames
- Series can be removed from DF with drop()

Data Aggregation
-describe()
-groupby()
-groupby([]) & unstack()
-mean(), sum(), median(),…

NaN Values & Replacing
-NaN is representation of null values
-series.describe() ignore NaN
-NaNs:
-remove drop()
-replace with default
- forward- or backwards-fill, interpolate

End Part 1
-DataSeries & DataFrame
-I/O
-Data analysis & aggregation
-Indexes
-Visualisation
-Interacting with the data

Part 2
A deeper look at the index with the TimeSeries Index
-TimeSeriesIndex
-pd.to_datetime() ! US date friendly
-Data Aggregation examples

before TimeSeries Index: unordered

Resampling
-H hourly frequency
-T minutely frequency
-S secondly frequency
-L milliseonds
-U microseconds
-N nanoseconds
-D calendar day frequency
-W weekly frequency
-M month end frequency
-Q quarter end frequency
-A year end frequency
- B business day frequency
- C custom business day frequency (experimental)
- BM business month end frequency
- CBM custom business month end frequency
- MS month start frequency
- BMS business month start frequency
- CBMS custom business month start frequency
- BQ business quarter endfrequency
- QS quarter start frequency
- BQS business quarter start frequency
- BA business year end frequency
- AS year start frequency
- BAS business year start frequency
- BH business hour frequency

Attributions
Panda Picture
By Ailuropoda at en.wikipedia (Transferred from en.wikipedia) [GFDL (http://www.gnu.org/copyleft/fdl.html), CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/
3.0/) or CC BY-SA 2.5-2.0-1.0 (http://creativecommons.org/licenses/by-sa/2.5-2.0-1.0)], from Wikimedia Commons

ah@koenigsweg.com
@hendorf
Code-Examples
https://github.com/Koenigsweg/data-timeseries-analysis-with-pandas

Introduction to Pandas and Time Series Analysis [PyCon DE]

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