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"""
----------------------------------------------------------------------
Filename : 01_basic_data_structs.py
Date : 12th Dec, 2013
Author : Jaidev Deshpande
Purpose : To get started with basic data structures in Pandas
Libraries: Pandas 0.12 and its dependencies
----------------------------------------------------------------------
"""
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. http://pandas.pydata.org
There are many useful objects in Pandas:
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# imports
import pandas as pd
from math import pi
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s = pd.Series(range(10))
print(s)
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print(s[5])
A pandas Series, like a list, doesn't have to be homogenous.
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s = pd.Series(['foo', None, 3+4j])
The index of a Series can be arbitrary as well.
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inds = ['bar',1, (1, 2)]
s.index = inds
print(s['bar'], s[1], s[(1, 2)])
Multiple Series objects can be clubbed together to make a pandas DataFrame. The pandas DataFrame is similar to the data.frame object in R.
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s1 = pd.Series(range(10))
s2 = pd.Series(range(10,20))
df = pd.DataFrame({'A':s1,'B':s2})
df.head()
Think of pandas DataFrames as dicts of Series. Almost all operations that are valid on a Python dictionary will work on a pandas DataFrame.
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df['C'] = [str(c) for c in range(20, 30)]
print(df.head())
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print(df['C'])
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del df['A']
print(df.head(10))
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df.update({'B': range(50,60)})
print(df.head())
Index objects available in Pandas:
Index : The most general Pandas index, often created by defaultInt64Index : Specialized index for integer valuesMultiIndex : Hierarchical indexDatetimeIndex: Nanosecond timestamps that can be used as indexesPeriodIndex : Specialized indices for timespans
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df.index
update method demonstrated above.