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Pandas in a nutshell

Posted on Jul 16, 2017 · 28 mins read
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Introduction

Pandas is an open source data analysis library in Python and it is extensively used for Data analysis, Data munging and Cleaning. Pandas has a high performing and user friendly data structures.

What makes Pandas a great choice for data analysis? It is it’s rich and highly performant data structures which are built on top of numpy and leverage it’s fast array operations. In thispost we will see how Pandas can be used efficiently for the data analysis and will explore some of it’s basic operations. Pandas has two types of data structures:

a) Series – it’s a one dimensional array with indexes, it stores a single column or row of data in a Dataframe

b) Dataframe – it’s a tabular spreadsheet like structure representing rows each of which contains one or multiple columns

First, Let’s do some import

<span class="kn" style="color: #008080;">import</span> <span class="nn" style="color: #0000ff;">pandas</span> <span class="k" style="color: #008080;">as</span> <span class="nn" style="color: #0000ff;">pd</span>
<span class="kn" style="color: #008080;">import</span> <span class="nn" style="color: #0000ff;">numpy</span> <span class="k" style="color: #008080;">as</span> <span class="nn" style="color: #0000ff;">np</span>
<span class="kn" style="color: #008080;">from</span> <span class="nn" style="color: #0000ff;">pandas</span> <span class="k" style="color: #008080;">import</span> <span class="n" style="color: #0000ff;">Series</span><span class="p">,</span> <span class="n" style="color: #0000ff;">DataFrame
</span>
<span class="kn" style="color: #008080;">import</span> <span class="nn" style="color: #0000ff;">pandas</span> <span class="k" style="color: #008080;">as</span> <span class="nn" style="color: #0000ff;">pd</span>
<span class="kn" style="color: #008080;">import</span> <span class="nn" style="color: #0000ff;">numpy</span> <span class="k" style="color: #008080;">as</span> <span class="nn" style="color: #0000ff;">np</span>
<span class="kn" style="color: #008080;">from</span> <span class="nn" style="color: #0000ff;">pandas</span> <span class="k" style="color: #008080;">import</span> <span class="n" style="color: #0000ff;">Series</span><span class="p">,</span> <span class="n" style="color: #0000ff;">DataFrame
</span>

Series

Create a Series with the list of values and default indexes

<span class="n">this</span><span class="o">=</span><span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">2</span></span><span class="p">])</span>
<span class="n">this</span><span class="o">=</span><span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">2</span></span><span class="p">])</span>

Check how the Series object looks like

<span class="n">this</span>
0 6
1 5
2 4
3 9
4 2
dtype: int6
<span class="n">this</span><span class="o">.</span><span class="n">values</span>
array([6, 5, 4, 9, 2])

Check the Index for this Series, Pandas generates the indexes automatically and default starts from 0

<span class="n">this</span><span class="o">.</span><span class="n">index</span>
Int64Index([0, 1, 2, 3, 4], dtype=’int64′)

Create a Series of US Cities with their Population and Index is set as the City for the Series, The Object takes the Indexes as defined with their corresponding column values i.e Population

citypopulation_2016 = Series([8537673,3976322,2704958,2303482,1615017],
index=[‘NYC’,‘Los Angeles’,‘Chicago’,‘Houston’,‘Phoenix’])
<span class="n">citypopulation_2016</span>
NYC 8537673
Los Angeles 3976322
Chicago 2704958
Houston 2303482
Phoenix 1615017
dtype: int64

Find Population lesser than 2.5 million

Find the rows with population lower than 2.5 million

<span class="n">citypopulation_2016</span><span class="p">[</span><span style="color: #993300;"><span class="n">citypopulation_2016</span><span class="o">&lt;</span><span class="mi">2500000</span></span><span class="p">]</span>
Houston 2303482
Phoenix 1615017
dtype: int64

Is Chicago in the Series

Check if Chicago is in the Series or not, it will return True or False boolean value

<span class="s">'Chicago'</span> <span class="ow">in</span> <span class="n">citypopulation_2016</span>
True

Create a list of Index

Create a list of Indexes with one extra value Philadelphia

<span class="n">cities</span> <span class="o">=</span> <span class="p">[</span><span style="color: #993300;"><span class="s">'NYC'</span><span class="p">,</span><span class="s">'Los Angeles'</span><span class="p">,</span><span class="s">'Chicago'</span><span class="p">,</span><span class="s">'Houston'</span><span class="p">,</span><span class="s">'Phoenix'</span><span class="p">,</span><span class="s">'Philadelhia'</span></span><span class="p">]</span>

Create a Series with this new Indexes, The value for Philadelhia is NaN (Not a Number)

<span class="n">Ser</span> <span class="o">=</span> <span class="n">Series</span><span class="p">(</span><span class="n">citypopulation_2016</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="n">cities</span><span class="p">)</span>
<span class="n">Ser</span>
NYC 8537673
Los Angeles 3976322
Chicago 2704958
Houston 2303482
Phoenix 1615017
Philadelhia NaN
dtype: float64

Philadelphia not in list that’s why it is null

Check what are the null values in the above Series

<span class="n">Ser</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span>
NYC False
Los Angeles False
Chicago False
Houston False
Phoenix False
Philadelhia True
dtype: bool

Adding two series

Add the two Series object Ser & citypopulation_2016 and store the sum to a new object Ser1

<span class="n">Ser1</span> <span class="o">=</span> <span class="n">Ser</span><span class="o">+</span><span class="n">citypopulation_2016</span>
<span class="n">Ser1</span>
Chicago 5409916
Houston 4606964
Los Angeles 7952644
NYC 17075346
Philadelhia NaN
Phoenix 3230034
dtype: float64

Name the Series

<span class="n">Ser1</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">"US Populate cities"</span>
<span class="n">Ser1</span>
Cities
Chicago 5409916
Houston 4606964
Los Angeles 7952644
NYC 17075346
Philadelhia NaN
Phoenix 3230034
Name: US Populate cities, dtype: float64

Name the Index

<span class="n">Ser1</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s">'Cities'</span>
<span class="n">Ser1</span>
Cities
Chicago 5409916
Houston 4606964
Los Angeles 7952644
NYC 17075346
Philadelhia NaN
Phoenix 3230034
Name: US Populate cities, dtype: float64

Dataframes

Dataframe has many functions to read the data from various sources like Json, CSV, Excel, HTML etc. ro dataframe object.Here we would be reading the Salary data from the CSV file

<span class="n">df</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">"<span style="color: #993300;">/Users/vbabu/Documents/personal/MyGitWork/csv/mls-salaries-2016.csv</span>"</span><span class="p">)</span>

Now using the ‘head’ function you can see the first five rows by default and similarly for the bottom 5 rows use ‘tail’ function

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
club last_name first_name position base_salary guaranteed_compensation
ATL Burgos Efrain M 62508 62508.0
ATL Oblitey Otoo Jeffrey M 51504 51504.0
ATL Tambakis Alexander GK 63000 63000.0
CHI Accam David F-M 700000 770937.5
CHI Alvarez Arturo F 115000 118264.0
<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
club last_name first_name position base_salary guaranteed_compensation
NaN Martinez Cristian M 62508.00 67008.00
NaN Moore Luke F 115000.00 137500.00
NaN Nyassi Sanna M 135000.00 141250.00
NaN Ovalle Adolfo M 63000.00 73500.00
NaN Tshuma Schillo F 81999.96 119999.96

Get the information about the dataframe using the ‘info’ function, Which gives details like field datatype, number of values, memory usage etc.

<class ‘pandas.core.frame.DataFrame’> Int64Index: 555 entries, 0 to 554 Data columns (total 6 columns): club 549 non-null object last_name 555 non-null object first_name 553 non-null object position 555 non-null object base_salary 555 non-null float64 guaranteed_compensation 555 non-null float64 dtypes: float64(2), object(4) memory usage: 30.4+ KB

Let’s check the data for a single Column ‘last_name”, Using ‘head’ function we will check the first five values for this column

<span class="n">df</span><span class="p">[</span><span class="s">'<span style="color: #993300;">last_name</span>'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
0 Burgos
1 Oblitey Otoo
2 Tambakis
3 Accam
4 Alvarez
Name: last_name, dtype: object

Add a New Column to Dataframe

Let’s add ‘Age’ column to the existing dataframe, You can see there is no value set for the new column so Pandas handles it internally and set the value as NaN

<span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'last_name'</span><span class="p">,</span><span class="s">'first_name'</span><span class="p">,</span><span class="s">'Age'</span></span><span class="p">])</span>
last_name first_name Age
Burgos Efrain NaN
Oblitey Otoo Jeffrey NaN
Tambakis Alexander NaN
Accam David NaN
Alvarez Arturo NaN
Calistri Joey NaN
Campbell Jonathan NaN
Cocis Razvan NaN
Conner Drew NaN
Doody Patrick NaN
Fernandez Collin NaN
Gehrig Eric NaN
Goossens John NaN
Harrington Michael NaN
Igboananike Kennedy NaN
Johnson Sean NaN
Junior Gilberto NaN
Kappelhof Johan NaN
LaBrocca Nick NaN
Lampson Matt NaN
McLain Patrick NaN
Meira Joao NaN
Morrell Alex NaN
Polster Matt NaN
Ramos Rodrigo NaN
Stephens Michael NaN
Thiam Khaly NaN
Vincent Brandon NaN
Afful Harrison NaN
Ashe Corey NaN
Carducci Marco NaN
Dean Christian NaN
Flores Deybi NaN
Froese Kianz NaN
Harvey Jordan NaN
Hurtado Erik NaN
Jacobson Andrew NaN
Kah Pa Modou NaN
Kudo Masato NaN
Laba Matias NaN
Manneh Kekuta NaN
McKendry Ben NaN
Mezquida Nicolas NaN
Morales Pedro NaN
Ousted David NaN
Parker Tim NaN
Perez Blas NaN
Rivero Octavio NaN
Seiler Cole NaN
Smith Jordan NaN
Techera Cristian NaN
Teibert Russell NaN
Tornaghi Paolo NaN
Waston Kendall NaN
Gargan Dan NaN
Martinez Cristian NaN
Moore Luke NaN
Nyassi Sanna NaN
Ovalle Adolfo NaN
Tshuma Schillo NaN

555 rows × 3 columns

Let’s see how we can get the value from the index, So here we want to get the 4th row value from the dataframe. All the columns of the 4th row is shown

<span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
club CHI
last_name Alvarez
first_name Arturo
position F
base_salary 115000
guaranteed_compensation 118264
Name: 4, dtype: object

Add a new column – base salary * 2

This is another example of adding a new column ‘Double_Salary’ which is double the ‘base_salary’ of existing column.

<span class="n">df</span><span class="p">[</span><span class="s">'Double_Salary'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s">'base_salary'</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
club last_name first_name position base_salary guaranteed_compensation Double_Salary taxes
ATL Burgos Efrain M 62508.00 62508.00 125016.00 Base Tax
ATL Oblitey Otoo Jeffrey M 51504.00 51504.00 103008.00 Base Tax
ATL Tambakis Alexander GK 63000.00 63000.00 126000.00 Base Tax
CHI Accam David F-M 700000.00 770937.50 1400000.00 Base Tax
CHI Alvarez Arturo F 115000.00 118264.00 230000.00 Base Tax
CHI Calistri Joey M 51500.00 51500.00 103000.00 Base Tax
CHI Campbell Jonathan D 70000.00 78125.00 140000.00 Base Tax
CHI Cocis Razvan M 160000.00 160000.00 320000.00 Base Tax
CHI Conner Drew M 62500.00 62500.00 125000.00 Base Tax
CHI Doody Patrick D 53471.00 53471.00 106942.00 Base Tax
CHI Fernandez Collin M 75000.00 77000.00 150000.00 Base Tax
CHI Gehrig Eric D 106875.00 112708.33 213750.00 Base Tax
CHI Goossens John M 200000.00 200000.00 400000.00 Base Tax
CHI Harrington Michael D 125000.00 125000.00 250000.00 Base Tax
CHI Igboananike Kennedy F 800000.00 901666.67 1600000.00 Base Tax
CHI Johnson Sean GK 250000.00 253000.00 500000.00 Base Tax
CHI Junior Gilberto F 1145000.00 1145000.00 2290000.00 Base Tax
CHI Kappelhof Johan D 480000.00 520000.00 960000.00 Base Tax
CHI LaBrocca Nick M 110000.00 110000.00 220000.00 Base Tax
CHI Lampson Matt GK 71250.00 76625.00 142500.00 Base Tax
CHI McLain Patrick GK 72500.00 72500.00 145000.00 Base Tax
CHI Meira Joao D-M 110000.00 126500.00 220000.00 Base Tax
CHI Morrell Alex M 51500.00 51500.00 103000.00 Base Tax
CHI Polster Matt D-M 84000.00 99000.00 168000.00 Base Tax
CHI Ramos Rodrigo D 80000.00 84000.00 160000.00 Base Tax
CHI Stephens Michael M 105000.00 115000.00 210000.00 Base Tax
CHI Thiam Khaly M 144000.00 144000.00 288000.00 Base Tax
CHI Vincent Brandon D 70000.00 91875.00 140000.00 Base Tax
CLB Afful Harrison D 275000.00 291666.67 550000.00 Base Tax
CLB Ashe Corey D 95000.00 105500.00 190000.00 Base Tax
VAN Carducci Marco GK 63000.00 63000.00 126000.00 Base Tax
VAN Dean Christian D 110000.00 191000.00 220000.00 Base Tax
VAN Flores Deybi M 62500.00 68385.00 125000.00 Base Tax
VAN Froese Kianz M 66000.00 70500.00 132000.00 Base Tax
VAN Harvey Jordan D 165000.00 165000.00 330000.00 Base Tax
VAN Hurtado Erik F/M 86091.50 121091.50 172183.00 Base Tax
VAN Jacobson Andrew M 62500.08 87500.08 125000.16 Base Tax
VAN Kah Pa Modou D 97000.00 101850.00 194000.00 Base Tax
VAN Kudo Masato F 310000.00 314000.00 620000.00 Base Tax
VAN Laba Matias M 560000.00 720500.00 1120000.00 Base Tax
VAN Manneh Kekuta M-F 127500.00 157000.00 255000.00 Base Tax
VAN McKendry Ben M 52500.00 52500.00 105000.00 Base Tax
VAN Mezquida Nicolas M-F 88000.00 88000.00 176000.00 Base Tax
VAN Morales Pedro M 1232500.00 1258900.00 2465000.00 Base Tax
VAN Ousted David GK 360000.00 378933.33 720000.00 Base Tax
VAN Parker Tim D 66000.00 84750.00 132000.00 Base Tax
VAN Perez Blas F 215000.00 215000.00 430000.00 Base Tax
VAN Rivero Octavio F 890850.00 890850.00 1781700.00 Base Tax
VAN Seiler Cole D 51500.00 51500.00 103000.00 Base Tax
VAN Smith Jordan D 115000.00 122743.00 230000.00 Base Tax
VAN Techera Cristian M 320000.00 345000.00 640000.00 Base Tax
VAN Teibert Russell M 115000.00 182500.00 230000.00 Base Tax
VAN Tornaghi Paolo GK 62500.00 62500.00 125000.00 Base Tax
VAN Waston Kendall D 300000.00 318125.00 600000.00 Base Tax
NaN Gargan Dan D 145000.00 145000.00 290000.00 Base Tax
NaN Martinez Cristian M 62508.00 67008.00 125016.00 Base Tax
NaN Moore Luke F 115000.00 137500.00 230000.00 Base Tax
NaN Nyassi Sanna M 135000.00 141250.00 270000.00 Base Tax
NaN Ovalle Adolfo M 63000.00 73500.00 126000.00 Base Tax
NaN Tshuma Schillo F 81999.96 119999.96 163999.92 Base Tax

Add specific values to the new column based on the indexes, So here for the indexes 0 & 6, the ‘taxes’ column value would be ‘Federal Tax’ & ‘State Tax’ and rest of the indexes will have the NaN value or null

<span class="n">tax</span> <span class="o">=</span> <span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="s">'Federal Tax'</span><span class="p">,</span><span class="s">'State Tax'</span></span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="mi">0</span><span class="p">,</span><span class="mi">6</span></span><span class="p">])</span>
<span class="n">df</span><span class="p">[</span><span class="s">'taxes'</span><span class="p">]</span> <span class="o">=</span> <span class="n">tax</span>
<span class="n">df</span>
club last_name first_name position base_salary guaranteed_compensation Double_Salary taxes
ATL Burgos Efrain M 62508.00 62508.00 125016.00 Federal Tax
ATL Oblitey Otoo Jeffrey M 51504.00 51504.00 103008.00 NaN
ATL Tambakis Alexander GK 63000.00 63000.00 126000.00 NaN
CHI Accam David F-M 700000.00 770937.50 1400000.00 NaN
CHI Alvarez Arturo F 115000.00 118264.00 230000.00 NaN
CHI Calistri Joey M 51500.00 51500.00 103000.00 NaN
CHI Campbell Jonathan D 70000.00 78125.00 140000.00 State Tax
CHI Cocis Razvan M 160000.00 160000.00 320000.00 NaN
CHI Conner Drew M 62500.00 62500.00 125000.00 NaN
CHI Doody Patrick D 53471.00 53471.00 106942.00 NaN
CHI Fernandez Collin M 75000.00 77000.00 150000.00 NaN
CHI Gehrig Eric D 106875.00 112708.33 213750.00 NaN
CHI Goossens John M 200000.00 200000.00 400000.00 NaN
CHI Harrington Michael D 125000.00 125000.00 250000.00 NaN
CHI Igboananike Kennedy F 800000.00 901666.67 1600000.00 NaN
CHI Johnson Sean GK 250000.00 253000.00 500000.00 NaN
CHI Junior Gilberto F 1145000.00 1145000.00 2290000.00 NaN
CHI Kappelhof Johan D 480000.00 520000.00 960000.00 NaN
CHI LaBrocca Nick M 110000.00 110000.00 220000.00 NaN
CHI Lampson Matt GK 71250.00 76625.00 142500.00 NaN
CHI McLain Patrick GK 72500.00 72500.00 145000.00 NaN
CHI Meira Joao D-M 110000.00 126500.00 220000.00 NaN
CHI Morrell Alex M 51500.00 51500.00 103000.00 NaN
CHI Polster Matt D-M 84000.00 99000.00 168000.00 NaN
CHI Ramos Rodrigo D 80000.00 84000.00 160000.00 NaN
CHI Stephens Michael M 105000.00 115000.00 210000.00 NaN
CHI Thiam Khaly M 144000.00 144000.00 288000.00 NaN
CHI Vincent Brandon D 70000.00 91875.00 140000.00 NaN
CLB Afful Harrison D 275000.00 291666.67 550000.00 NaN
CLB Ashe Corey D 95000.00 105500.00 190000.00 NaN
VAN Carducci Marco GK 63000.00 63000.00 126000.00 NaN
VAN Dean Christian D 110000.00 191000.00 220000.00 NaN
VAN Flores Deybi M 62500.00 68385.00 125000.00 NaN
VAN Froese Kianz M 66000.00 70500.00 132000.00 NaN
VAN Harvey Jordan D 165000.00 165000.00 330000.00 NaN
VAN Hurtado Erik F/M 86091.50 121091.50 172183.00 NaN
VAN Jacobson Andrew M 62500.08 87500.08 125000.16 NaN
VAN Kah Pa Modou D 97000.00 101850.00 194000.00 NaN
VAN Kudo Masato F 310000.00 314000.00 620000.00 NaN
VAN Laba Matias M 560000.00 720500.00 1120000.00 NaN
VAN Manneh Kekuta M-F 127500.00 157000.00 255000.00 NaN
VAN McKendry Ben M 52500.00 52500.00 105000.00 NaN
VAN Mezquida Nicolas M-F 88000.00 88000.00 176000.00 NaN
VAN Morales Pedro M 1232500.00 1258900.00 2465000.00 NaN
VAN Ousted David GK 360000.00 378933.33 720000.00 NaN
VAN Parker Tim D 66000.00 84750.00 132000.00 NaN
VAN Perez Blas F 215000.00 215000.00 430000.00 NaN
VAN Rivero Octavio F 890850.00 890850.00 1781700.00 NaN
VAN Seiler Cole D 51500.00 51500.00 103000.00 NaN
VAN Smith Jordan D 115000.00 122743.00 230000.00 NaN
VAN Techera Cristian M 320000.00 345000.00 640000.00 NaN
VAN Teibert Russell M 115000.00 182500.00 230000.00 NaN
VAN Tornaghi Paolo GK 62500.00 62500.00 125000.00 NaN
VAN Waston Kendall D 300000.00 318125.00 600000.00 NaN
NaN Gargan Dan D 145000.00 145000.00 290000.00 NaN
NaN Martinez Cristian M 62508.00 67008.00 125016.00 NaN
NaN Moore Luke F 115000.00 137500.00 230000.00 NaN
NaN Nyassi Sanna M 135000.00 141250.00 270000.00 NaN
NaN Ovalle Adolfo M 63000.00 73500.00 126000.00 NaN
NaN Tshuma Schillo F 81999.96 119999.96 163999.92 NaN

Delete Column

‘taxes’ column deleted from the dataframe

<span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="s">'taxes'</span><span class="p">]</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
club last_name first_name position base_salary guaranteed_compensation Double_Salary
ATL Burgos Efrain M 62508 62508.0 125016
ATL Oblitey Otoo Jeffrey M 51504 51504.0 103008
ATL Tambakis Alexander GK 63000 63000.0 126000
CHI Accam David F-M 700000 770937.5 1400000
CHI Alvarez Arturo F 115000 118264.0 230000

Dataframe from Dictionary

<span class="n">Myfruits</span> <span class="o">=</span> <span class="p">{</span><span class="s">'fruits'</span><span class="p">:[</span><span style="color: #993300;"><span class="s">'Orange'</span><span class="p">,</span><span class="s">'Mango'</span><span class="p">,</span><span class="s">'Grapes'</span><span class="p">,</span><span class="s">'Guava'</span></span><span class="p">],</span><span class="s">'Price'</span> <span class="p">:[</span><span style="color: #993300;"><span class="mi">20</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">100</span></span><span class="p">]}</span>
<span class="n">dffruits</span><span class="o">=</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">Myfruits</span><span class="p">)</span>
<span class="n">dffruits</span>
Price fruits
20 Orange
50 Mango
90 Grapes
100 Guava

Reindexing

Create a new Series for colors as index

<span class="n">df</span> <span class="o">=</span> <span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">7</span></span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'Red'</span><span class="p">,</span><span class="s">'Blue'</span><span class="p">,</span><span class="s">'Green'</span><span class="p">,</span><span class="s">'Brown'</span><span class="p">,</span><span class="s">'Violet'</span></span><span class="p">])</span>
<span class="n">df</span>
Red       6
Blue      4
Green     5
Brown     9
Violet    7
dtype: int64

Re-index and add a new index ‘Pink’ in the existing Series, Therefore the value of index ‘Pink’ is null

<span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">([</span><span style="color: #993300;"><span class="s">'Red'</span><span class="p">,</span><span class="s">'Blue'</span><span class="p">,</span><span class="s">'Green'</span><span class="p">,</span><span class="s">'Brown'</span><span class="p">,</span><span class="s">'Violet'</span><span class="p">,</span><span class="s">'Pink'</span></span><span class="p">])</span>
Red        6
Blue       4
Green      5
Brown      9
Violet     7
Pink     NaN
dtype: float64

Fill the null values with a default value

<span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">([</span><span style="color: #993300;"><span class="s">'Red'</span><span class="p">,</span><span class="s">'Blue'</span><span class="p">,</span><span class="s">'Green'</span><span class="p">,</span><span class="s">'Brown'</span><span class="p">,</span><span class="s">'Violet'</span><span class="p">,</span><span class="s">'Pink'</span></span><span class="p">],</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
Red        6
Blue       4
Green      5
Brown      9
Violet     7
Pink      10
dtype: int64

Reindexing rows, columns or both

Create a Dataframe with random integers and index value as defined below and columns also as shown

<span class="n">df</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)),</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'A'</span><span class="p">,</span><span class="s">'B'</span><span class="p">,</span><span class="s">'D'</span><span class="p">,</span><span class="s">'E'</span><span class="p">,</span><span class="s">'F'</span></span><span class="p">],</span>
                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'col1'</span><span class="p">,</span><span class="s">'col2'</span><span class="p">,</span><span class="s">'col3'</span><span class="p">,</span><span class="s">'col4'</span><span class="p">,</span><span class="s">'col5'</span></span><span class="p">])</span>


<span class="n">df</span>
col1 col2 col3 col4 col5  
A 17 3 16 16 18
B 2 24 5 3 15
C 20 22 5 21 16
D 13 13 10 3 20
E 15 18 20 2 2

Summing up columns and rows

Sum along the rows by using axis =1

<span class="n">df</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
A    70
B    49
D    84
E    59
F    57
dtype: int64

Sum along the rows by using axis=0

<span class="n">dframe</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
col1    65
col2    60
col3    53
col4    82
col5    77
dtype: int64

Simple Stats

<span class="n">dframe</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
col1 col2 col3 col4 col5
5.000000 5 5.000000 5.000000 5.00000
13.000000 12 10.600000 16.400000 15.40000
8.774964 10 5.549775 5.727128 8.70632
1.000000 2 1.000000 9.000000 2.00000
8.000000 3 11.000000 15.000000 12.00000
16.000000 11 13.000000 16.000000 18.00000
16.000000 19 13.000000 17.000000 21.00000
24.000000 25 15.000000 25.000000 24.00000

Find the correlation between each of the variables of the dataframe using ‘corr’ function

<span class="n">corr</span><span class="o">=</span><span class="n">dframe</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>

Plot the Correlation heatmap matrix using Seaborn – A Data Visualization Library

<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">corr</span><span class="p">)</span>

Missing Values

<span class="n">df</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">([[</span><span style="color: #993300;"><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">7</span></span><span class="p">],[</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">NAN</span><span class="p">,</span><span class="mi">9</span></span><span class="p">],[</span><span style="color: #993300;"><span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><spn class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">NaN</span></span><span class="p">]])</span>

<span class="n">df</span>
  0 1 2 3 4
0 5 4 3 2 7
1 6 3 NaN NaN 9
2 NaN 3 4 2 NaN

drop all the columns where null value is available

<span class="n">df1</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">df1</span>

|1|0| |— |— | |0|4| |1|3| |2|3|

Show rows with atleast ‘thresh’ no. of values, Since all the rows contains minimum three non null values that’s why all rows are displayed

<span class="n">df2</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">thresh</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">df2</span>
  0 1 2 3 4
0 5 4 3 2 7
1 6 3 NaN NaN 9
2 NaN 3 4 2 NaN

fillna

fill nulls with values

<span class="n">df</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s">'5'</span><span class="p">)</span>
  0 1 2 3 4
0 5 4 3 2 7
1 6 3 5 5 9
2 5 3 4 2 5

Drop rows

<span class="n">dframe</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s">'B'</span><span class="p">)</span>
col1 col2 col3 col4 col5
8 2 1 15 18
16 3 11 9 12
16 11 13 25 24
1 25 13 17 21

Selecting only 2 columns

<span class="n">dframe</span><span class="p">[[</span><span class="s">'col1'</span><span class="p">,</span><span class="s">'col2'</span><span class="p">]]</span>
col1 col2
8 2
24 19
16 3
16 11
1 25

Selecting Index

<span class="n">dframe</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="s">'D'</span><span class="p">]</span>
col1    16
col2     3
col3    11
col4     9
col5    12
Name: D, dtype: int64

Selecting on Condition

<span class="n">dframe</span><span class="p">[</span><span class="n">dframe</span><span class="p">[</span><span class="s">'col1'</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">10</span><span class="p">]</span>

Out[207]: col1 col2 col3 col4 col5 B 24 19 15 16 2 D 16 3 11 9 12 E 16 11 13 25 24

Selecting values

<span class="n">df</span><span class="o">=</span><span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span></span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'A'</span><span class="p">,</span><span class="s">'B'</span><span class="p">,</span><span class="s">'C'</span><span class="p">,</span><span class="s">'D'</span></span><span class="p">])</span>
<span class="n">df</span>
A    6
B    4
C    3
D    2
dtype: int64

Select the Index ‘C’ value

<span class="n">df</span><span class="p">[</span><span class="s">'C'</span><span class="p">]</span>
3
1
3

Select First two values

<span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
A    6
B    4
dtype: int64

Sorting

Sort will be done by default on the Indexes, so before sorting index was P,R,S,Q and after sorting it changed to P,Q,R,S

<span class="n">df</span><span class="o">=</span><span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span></span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span style="color: #993300;"><span class="s">'P'</span><span class="p">,</span><span class="s">'R'</span><span class="p">,</span><span class="s">'S'</span><span class="p">,</span><span class="s">'Q'</span></span><span class="p">])</span>
<span class="n">df</span>
P    6
R    4
S    2
Q    1
dtype: int64
<span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
P    6
Q    1
R    4
S    2
dtype: int64

Order by values

<span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span>
Q    1
S    2
R    4
P    6
dtype: int64
<span class="n">df</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span>
P    4
R    3
S    2
Q    1
dtype: float64

MultiIndexing

Here we have first index as 1,2,3 and the second index has a,b for all the first index values, as shown here

<span class="n">Sr</span> <span class="o">=</span> <span class="n">Series</span><span class="p">([</span><span style="color: #993300;"><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span></span><span class="p">],</span><span class="n">index</span><span class="o">=</span><span class="p">[[</span><span style="color: #993300;"><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span></span><span class="p">],[</span><span style="color: #993300;"><span class="s">'a'</span><span class="p">,</span><span class="s">'b'</span><span class="p">,</span><span class="s">'a'</span><span class="p">,</span><span class="s">'b'</span><span class="p">,</span><span class="s">'a'</span><span class="p">,</span><span class="s">'b'</span></span><span class="p">]])</span>
<span class="n">Sr</span>
1  a    5
   b    4
2  a    3
   b    7
3  a    9
   b    6
dtype: int64

Verify the Index Levels

<span class="n">Sr</span><span class="o">.</span><span class="n">index</span>
MultiIndex(levels=[[<span style="color: #993300;">1, 2, 3</span>], [<span style="color: #993300;">'a', 'b'</span>]],
           labels=[[<span style="color: #993300;">0, 0, 1, 1, 2, 2</span>], [<span style="color: #993300;">0, 1, 0, 1, 0, 1</span>]])

Get Values by first Index

<span class="n">Sr</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
a    9
b    6
dtype: int64

Get Values by Second Index

<span class="n">Sr</span><span class="p">[:,</span><span class="s">'b'</span><span class="p">]</span>
1    4
2    7
3    6
dtype: int64
<span class="n">Sr</span><span class="o">.</span><span class="n">unstack</span><span class="p">()</span>
a b
5 4
3 7
9 6

Group By

Read a CSV file with the data of Booker Prize Winners from 1996-2016

<span class="n">df</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">'<span style="color: #993300;">./Desktop/Reports-Delete/Booker-Prize.csv</span>'</span><span class="p">)</span>

Get the first 3 rows of the dataset

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
Year Author Title Genre(s) Country Unnamed: 5 Unnamed: 6
1969 P. H. Newby Something to Answer For Novel United Kingdom NaN NaN
1970 Bernice Rubens The Elected Member Novel United Kingdom NaN NaN
1970 J. G. Farrell Troubles Novel United Kingdom\nIreland NaN NaN

Groupby Country, Author & Genre to know who are the Authors won the title more than once and which is the country won the Award maximum number of time

<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span style="color: #993300;"><span class="s">'Country'</span><span class="p">,</span><span class="s">'Author'</span><span class="p">,</span><span class="s">'Genre(s)'</span></span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s">'<span style="color: #993300;">Year</span>'</span><span class="p">],</span><span class="n">ascending</span><span class="o">=</span><span class="p">[</span><span class="k">False</span><span class="p">])</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
      Year Title  
Country Author Genre(s)      
Australia Peter Carey Historical novel 2 2  
United Kingdom\nIreland J. G. Farrell Novel 2 2  
United Kingdom Hilary Mantel Historical novel 2 2  
South Africa J. M. Coetzee Novel 2 2  
United Kingdom Kazuo Ishiguro Historical novel   1 1

Pandas is an amazing library for data analysis and I have shown some of the basic stuffs to learn but to get more hands on with the library and it’s functions, Use Varied set of data and practice a lot. The only mantra to learn Pandas and gain expertise is Practice, Practice & Practice