Real World Data AnalysisPANDAS

PYTHON PACKAGE FOR FAST, FLEXIBLE, AND EXPRESSIVE DATA STRUCTURES

DR. SYED IMTIYAZ HASSANASSISTANT PROFESSOR, DEPARTMENT. OF CSE, JAMIA HAMDARD(DEEMED TO BE UNIVERSITY), NEW DELHI, INDIA.https://syedimtiyazhassan.orgs.imtiyaz@jamiahamdard.ac.inhttp://www.jamiahamdard.edu

INTRODUCTION

For fast, flexible, and expressive data structures.

Designed to make working with “relational” or “labeled” data.

Prepared from:

https://pandas.pydata.org/pandas-docs/stable/getting_started/index.html

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WELL SUITED FOR

Tabular data with heterogeneously-typed columns.

Ordered and unordered (not necessarily fixed-frequency) time series data.

Arbitrary matrix data (homogeneously typed orheterogeneous) with row and column labels.

Any other form of observational / statistical data sets.

The data actually need not be labeled at all to beplaced into a pandas data structure.

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DATA STRUCTURES

Series: 1D labeled homogeneously-typed array.

DataFrame: General 2D labeled, size-mutabletabular structure with potentially heterogeneously-typed column.

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• import numpy as np

• import pandas as pd

• s = pd.Series([1, 3, 5, np.nan, 6, 8])

• s

SERIES

A Series by passing a list of values, letting pandascreate a default integer index.

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import numpy as npimport pandas as pds = pd.Series([1, 3, 5, np.nan, 6, 8])s

OBJECT CREATION

A DataFrame by passing a NumPy array, with a:

datetime index and

labeled columns.

NumPy arrays have one dtype for the entire array, while pandasDataFrames have one dtype per column.

labeled columns

df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))df

datetime index

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dates = pd.date_range('20130101', periods=6)dates

OBJECT CREATION A DataFrame by passing a dict of objects that can be converted

to series-like.

DataFrame

df2 = pd.DataFrame({'A': 1.,'B': pd.Timestamp('20130102'),'C': pd.Series(1, index=list(range(4)), dtype='float32'),'D': np.array([3] * 4, dtype='int32'),'E': pd.Categorical(["test", "train", "test", "train"]),'F': 'foo'})

df2

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df2.dtypes

VIEWING DATA df.head()

df.tail(3)

df.index

df.columns

df.describe()

df.T

df.to_numpy()

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SORTING

By Axis

By Values

By Axis

df.sort_index(axis=1, ascending=False)

datetime index

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df.sort_values(by='B')

SELECTION

Getting Selection by Label

df.loc

df.at

Selection by Position df.iloc

df.iat

Boolean Indexing

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GETTING Selecting a single column, which yields a Series, equivalent todf.A

df['A']

Selecting via [], which slices the rows.

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df[0:3]

df['20130102':'20130104']

df

df.A

SELECTION BY LABEL

Selecting on a multi-axis by label.

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df1 = pd.DataFrame(np.random.randn(6, 4))

df1.loc[0]

df.loc[dates[0]]

df.loc[:, ['A', 'B']]

df.loc['20130102':'20130104', ['A', 'B']]

df.loc['20130102', ['A', 'B']]

df.loc[dates[0], 'A']

df.at[dates[0], 'A'] #Fast

SELECTION BY POSITION

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df.iloc[3]

df.iloc[3:5, 0:2]

df.iloc[[1, 2, 4], [0, 2]]

df.iloc[1:3, :]

df.iloc[:, 1:3]

df.iat[1, 1]

df.iloc[1, 1]

BOOLEAN INDEXING

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df[df.A > 0] df[df > 0]

df2 = df.copy()df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']df2

df2[df2['E'].isin(['two', 'four'])]

SETTING

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df['F'] = s1s1

df.iat[0, 1] = 0df

s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6))

df.at[dates[0], 'A'] = 0df

df2 = df.copy()df2[df2 > 0] = -df2df2

MISSING DATA

df1 = df.copy()df1.dropna(how='any')

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df1.fillna(value=5)

pd.isna(df1)

Drop any rows that have missing data.

Filling missing data.

Get the Boolean mask where values are nan.

OPERATIONS

Stats

Apply

Concat

Join

Append

Grouping

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STATSAPPLY

df.mean()

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df.mean(1)

df.apply(np.cumsum)

Same operation on the other axis

Operations in general exclude missing data.

df.apply(lambda x: x.max() - x.min())

Applying functions to the data.

HISTOGRAM

s = pd.Series(np.random.randint(0, 7, size=10))s

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s.value_counts()

CONCAT

df = pd.DataFrame(np.random.randn(10, 4))

df

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pieces = [df[:3], df[3:7], df[7:]]pieces

pd.concat(pieces)

JOIN

left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})left

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right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

right

pd.merge(left, right, on='key')

JOIN

left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})left

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right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})right

pd.merge(left, right, on='key')

APPEND

df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])df

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s = df.iloc[3]s

df.append(s, ignore_index=True)

GROUPING

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar','foo', 'bar', 'foo', 'foo'],'B': ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],'C': np.random.randn(8),'D': np.random.randn(8)})

df

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df.groupby('A').sum()

df.groupby(['A', 'B']).sum()

PLOTTING

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PLOTTING

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df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,columns=['A', 'B', 'C', 'D'])

df

df.plot()

df.plot()

df = df.cumsum()

DATA FILES

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Format Type Data Description Reader Writer

text CSV read_csv to_csv

text JSON read_json to_json

text HTML read_html to_html

text Local clipboard read_clipboard to_clipboard

binary MS Excel read_excel to_excel

binary HDF5 Format read_hdf to_hdf

binary Feather Format read_feather to_feather

binary Parquet Format read_parquet to_parquet

binary Msgpack read_msgpack to_msgpack

binary Stata read_stata to_stata

binary SAS read_sas

binary Python Pickle Format read_pickle to_pickle

SQL SQL read_sql to_sql

SQL Google Big Query read_gbq to_gbq

df.to_csv('foo.csv')

pd.read_csv('foo.csv')

pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])

df.to_excel('foo.xlsx', sheet_name='Sheet1')

THANK YOU