Pandas Data Visualization (BMAN73701) PDF

I MAN,CHESTER_ I 1s24 The University of Man c'hester Alliance Manchester Business Schoo BMAN73701 Programming in ~ python™for Business Analytics Python Week 4: Lecture 1 Tabular Data (Pandas) and Data Visualisation...

I MAN,CHESTER_ I 1s24 The University of Man c'hester Alliance Manchester Business Schoo BMAN73701 Programming in ~ python™for Business Analytics Python Week 4: Lecture 1 Tabular Data (Pandas) and Data Visualisation Prof. Manuel López-Ibáñez [email protected] Office hours: Mon 4pm-5pm, Fri 9am-10am https://calendly.com/manuel-lopez-ibanez  I MANCH ESTER 1~2---l Agenda The Un iversity of Manchester Alliance Manchester Business Schoo,! Intro to Pandas Intro to Data Visualisation Matplotlib + Pandas Matplotlib in detail Programming visualisations BMAN73701 Week 4 3  I MAN,CHESTER_ I 1s24 The University of Man c'hester Alliance Manchester Business Schoo BMAN73701 Programming in ~ python™for Business Analytics Python Week 4: Lecture 1 Tabular Data (Pandas) and Data Visualisation Part 1: Tabular Data using Pandas Part 2: Data Visualisation  MAN,CHEsTER_ 1824 Intro to Pandas The University of Man c'hester Alliance Manchester Business Schoo Dt11t1 \f1ra 11glhrg u·itb Pandas. N11111Py, a nd f Py tbo 11 pandas Yit = /3' Xit + /J,i + Eit Python library for tabular data manipulation and analysis Strong support for time-series and visualisation Website: http://pandas.pydata.org/ O'REILLY" Wes McKinney Documentation: http://pandas.pydata.org/pandas-docs/stable/ import pandas as pd # Just a shortcut. BMAN73701 Week 4 5  MAN,CHEsTER_ 1824 pandas Basics The University of Man c'hester Alliance Manchester Business Schoo Basic structure: DataFrame – A table with columns and rows – Columns have “names” (labels), they are obligatory – Rows have “names” (index), they are generated if not given Date Fuel_Price IsHoliday 0 2010-02-05 0.1 True 1 2010-02-12 0.2 False 2 2010-02-19 0.3 True 3 2010-02-26 0.4 True 4 2010-03-05 0.5 True BMAN73701 Week 4 6  MANCHESTER 1824 From Lists, Dicts, … to DataFrame The University of Manchester Alliance Manchester Business School From a list In [2 ] : data = [ [ a 1 ] [ b , 2 ] ] In [3 ] : pd. Data Fr a me ( data , colu mns =: [ model , p r ice ] ) Out [ :3]: - model p r.1ce 0 a 1 1 b 2 From a dictionary In [ 4 ] : data = { model 1 : [ a , b ] p r ice : [ 1 2 ] } In [.5,] : pd. Data1F r a me ( data ) Out [ 5] : model p r·ice 0 a ·1 1 b 2 BMAN73701 Week 4 7  MAN,CHEsTER_ 1824 pandas Read/Write The University of Man c'hester Alliance Manchester Business Schoo Date Fuel_Price IsHoliday Unemployment 2010-02-05 0.1 True 8.625 2010-02-12 0.2 False 8.625 2010-02-19 0.3 True 8.335 2010-02-26 0.4 True 2010-03-05 0.5 True 8.335 Read from / Write to Excel in Comma-Separated-Values (CSV) format import pandas as pd df = pd.read_csv('filename.csv') df.to_csv('filename.csv') BMAN73701 Week 4 8  MANCHESTER 1824 From Excel to DataFrame The University of Manchester Alliance Manchester Business School In [l]: i mpo r t pandas as pd In : df = pd. r ead_cs v( C: /wo r k/ sto r el f eatu r es.csv.zip 1 1 ) In : df. head () Out [:3] '' Dat e St o r·e Tempe r·atu r·e Fu el P r.1ce Ma rk Downl Ma rk DO'!,.f n2 \ 0 2010-02-05 1 42.31 2.572 Na N Na N 1 2010-02- 12 1 38.51 2.5 48 Na N Na N 2 281181-812- 19 1 39.93 2.5 14 Na N Na N 3 281181-02-26 1 46.63 2._'ii::;;- "J t:i 1 Na N Na N 4 2010-03-05 1 46.50 2.625 Na N Na N Ma r kDown3 Ma r kDown4 Ma r kDown5 CPI Unempl oyme nt Is Hol iday 81 Na N Na N Na N 211.096358 8. 106 Fal se 1 Na N Na N Na N 211.2 42170 8. 106 True 2 Na N Na N Na N 211.289143 8. 106 Fal se 3 Na N Na N Na N 211.3196 43 8. 106 Fal se 4 Na N Na N Na N 211.350143 8. 106 Fal se In : t ype (df) Out [ 4] : pa ndas. co r e. f r·ame. Dat alF r·ame BMAN73701 Week 4 9  MANCHESTER 1824 From Excel to DataFrame The University of Manchester Alliance Manchester Business School I n [.5 ] : df. in-fo{) · I 1 Rangeindex: 182 ent r·ies, 0 to 181 Data columns {total 11.2 columns): Date 11.82 non-null object Sto r e ].82 non-null int64 Tempe r atu r e 182 non-null f loat6 4 Fuel IP r·ice 182 non- null f loat6 4 Ma r kDownl 90 non-null f loat6 4 Ma r kDown2 73 non-null f loat6 4 Ma r kDown3 89 non-null f loat6 4 Ma r kDown4 90 non-null f loat6 4 Ma r kDown5 90 non-null f loat6 4 CPI ].69 non-null float64 Unemployment 11.69 non-null f loat6 4 IsHoliday ].82 non-null bool dtypes: bool(1 ) , f loat6 4{9), int64{1L), object{:n.) 1memo r·y us age: 15. 9+· KB BMAN73701 Week 4 10  MANCHESTER 1824 The Index The University of Manchester Alliance Manchester Business School In [-6 ] : df = pd. r ead_cs v( 1 C: /wo r k/ s t o r el f ea tu r es.csv. z ip 1 ). df [ 1 Da t e 1 ] = pd. to_da t e t i me(d f [ 1 Dat e 1 ] ). df. i nde x = df [ 1 Da t e 1 ]. df. head (). Out ' ' Dat e St o r e Tempe r a tu r·e Fu el Pr ice Ma r kDownl Ma r kDown2 \ Da t e 2010-02-05 2010-02-05 1 42.31 2.572 Na N Na N 2010-02- 12 2010-02- 12 1 38.51 2.5 48 Na N Na N 2010-02- 19 2010-02- 19 1 ~9 g ·::i, ,.J... ,.J 2.514 Na N Na N 2010-02-26 2010-02-2 6 1 46.63 2.561 Na N Na N 2010-03-05 2010-03-05 1 46.50 2.625 NaN Na N BMAN73701 Week 4 11  MANCHESTER 1824 The Index The University of Manchester Alliance Manchester Business School In [6 ] : df = pd. r ead_csv( C: /wor k/s t or el fea tu res.csv. zip 1 1 )... : df [ Dat e 1 = pd. to_datetime(d f [ Dat e 1 ] 1 1 ] )... : df. index = df [ Dat e ]... : df. head () In [7 ] : df. i nf o,() 1 1 ID atetime i ndex: 182 ent ri es , 2010-02-05 to 2013-07-26 Data columns (total 12 columns): Date 182 non-null datetime6 4 [nis] Sto r e 182 non-null int64 Tempe r atu r e 182 non-null f 1oat64 Fuel Pr ice 182 non-null f loat6 4 IMa r·kD01-.rnl 90 non-null f 1oat64 IMa r·lkD01aJ"n2 73 non-nul 1 f loat6 4 IMa r·kD01--1n3 89 non-nul 1 f 1oat64 IMa r·1 BMAN73701 Week 4 83  :!::: r iii' -I ::I-:::,- ~ g;i 8 rti in ~~ ~ ~- g. ~ MASERA m~ iQ. Hierarchical clustering Tree diagrams -------------------, g;'~ !!!'. :::J ~ g. v,. ro FORDP "' Ul- V, n.., :::r rti DUSTER g CAM.ARC 0 0 0..,. -------------- , I di a.. C di ::s DODGE a.../J,' 'f" JA Q· fQ 'ONDA ~. -3, BMAN73701 Week 4 TOYOTA. FIAT1 w (Cl· FIATX FERRAR a C. LOTUS ER.C2 VOLVO - ~ ti! DATSU TOYOTA PORSCH MERC2 Pt.i\A.ZDA DA MERC2 MFR,r.;? Dendograms 84  MAN,CHEsTER_ 1824 Treemaps Total: $589M Poultry Meat Coconuts, Brazil Nuts, and Cashews 12.12% Rice Other Vegetable 1.51% Residues ,=====,I I ,_________.I II~ Treemap of Benin's exports by product category, BMAN73701 2009. Week 4 H~ ED 85  MANCHESTER 1824 Relationships: Chord diagram The Un iversity of Manchester Alliance Manchester Business Schoo,! 25000 25000 I L ' '> 'b0 0 BMAN73701 Week 4 86  MAN,CHEsTER_ 1824 Networks/Graphs The University of Man c'hester Alliance Manchester Business Schoo https://networkx.github.io/documentation/stable/ BMAN73701 Week 4 87  MAN,CHEsTER_ 1824 Geographical Data: Contour maps The University of Man c'hester Alliance Manchester Business Schoo '".. _,._ 95 + Endin R d1 0 s6j&v nn level 600 400 200 Matplotlib Basemap Toolkit: all https://matplotlib.org/basemap/stable/users/examples.html BMAN73701 Week 4 88 u... t  MANCH ESTER_ Geographical Data: Choropleth Maps I I 1~2➔ The University of Man c'h ester Alliance Manchester Business Schoo 20 11 US Agri culture Exports by State (Hover fo r breakdown) Millions USD 16k 14k 12k 10k ~ Bk 6k 4k 2k https://plotly.com/python/choropleth-maps/ BMAN73701 Week 4 89