Turning Data Into Decisions Bar charts, histograms, scatter plots, subplots, and plotting straight from pandas Previously learned to draw a line — literally. we now know how to create a figure, style it, and save it. But real analyst work rarely stops at trends over time. You'll need to compare categories , understand distributions , spot relationships between variables , and show several views of the data at once . That's exactly what today covers. Grab a coffee — let's turn raw numbers into charts that actually tell a story. 1. Bar Charts: Comparing Categories When to use one Bar charts are your go-to whenever you're comparing discrete categories against each other — regions, products, departments, months. If someone asks "which one is bigger?", a bar chart answers it instantly. The code import matplotlib.pyplot as plt regions = [ " North " , " South " , " East " , " West " ] revenue = [ 420 , 380 , 510 , 290 ] fig , ax = plt . subplots ( figsize = ( 7 , 5 )) ax . bar ( regions , revenue , color = " teal " ) ax . set_title ( " Revenue by Region " ) ax . set_xlabel ( " Region " ) ax . set_ylabel ( " Revenue (K) " ) plt . show () Rule of thumb: categories on the x-axis → bar() . Long labels or many categories → barh() . 2. Histograms: Understanding Distributions Bar chart vs. histogram — don't mix them up This trips up almost every beginner: a bar chart compares separate categories. A histogram shows how continuous numeric data is distributed by grouping values into ranges called bins . There are no gaps between histogram bars by convention, because the x-axis is continuous, not categorical. The code import matplotlib.pyplot as plt ages = [ 22 , 25 , 25 , 28 , 30 , 31 , 31 , 31 , 35 , 36 , 38 , 40 , 42 , 45 , 45 , 48 , 50 , 52 , 55 , 60 ] fig , ax = plt . subplots ( figsize = ( 7 , 5 )) ax . hist ( ages , bins = 6 , color = " steelblue " , edgecolor = " black " ) ax . set_title ( " Customer Age Distribution " ) ax . set_xlabel ( " Age " ) ax . set_ylabel ( " Number of Customers " ) plt . show () Why bins matters Too few bins hides detail; too many creates noise. bins=6 groups your 20 data points into 6 age ranges. Try changing it to bins=3 and bins=15 and watch the story the chart tells actually change — this is a great instinct to build early. Use case: salary bands, order sizes, response times, test scores — anywhere you want to see the shape of the data (skewed? normal? clustered?). 3. Scatter Plots: Spotting Relationships When to use one Scatter plots answer "does X affect Y?" — like advertising spend vs. sales, or hours studied vs. exam score. Each point is one observation with two numeric values. The code import matplotlib.pyplot as plt ad_spend = [ 10 , 15 , 20 , 25 , 30 , 35 , 40 , 45 ] sales = [ 22 , 25 , 33 , 38 , 44 , 50 , 58 , 63 ] fig , ax = plt . subplots ( figsize = ( 7 , 5 )) ax . scatter ( ad_spend , sales , color = " crimson " ) ax . set_title ( " Ad Spend vs Sales " ) ax . set_xlabel ( " Ad Spend (K) " ) plt . show () Bonus: a third dimension with size or color Scatter plots can secretly show a third variable by varying marker size ( s= ) or color ( c= ): customer_count = [ 5 , 8 , 12 , 15 , 18 , 22 , 27 , 30 ] fig , ax = plt . subplots ( figsize = ( 7 , 5 )) scatter = ax . scatter ( ad_spend , sales , s = [ c * 10 for c in customer_count ], c = customer_count , cmap = " viridis " ) ax . set_title ( " Ad Spend vs Sales (bubble size = customers) " ) ax . set_xlabel ( " Ad Spend (K) " ) plt . colorbar ( scatter , label = " Customer Count " ) plt . show () This is called a bubble chart — a nice trick to have once you're comfortable with the basics. 4. Subplots: Multiple Charts, One Figure Why you need this Reports rarely show just one chart. Subplots let you lay out several plots in a grid inside a single figure — perfect for dashboards or side-by-side comparisons. The code import matplotlib.pyplot as plt fig , axes = plt . subplots ( 1 , 2 , figsize = ( 12 , 5 )) # 1 row, 2 columns # Left chart axes [ 0 ]. bar ( regions , revenue , color = " teal " ) axes [ 0 ]. set_title ( " Revenue by Region " ) # Right chart axes [ 1 ]. hist ( ages , bins = 6 , color = " steelblue " , edgecolor = " black " ) axes [ 1 ]. set_title ( " Customer Age Distribution " ) plt . tight_layout () plt . show () The pattern to remember plt.subplots(rows, cols) gives you back an array of axes — axes[0] , axes[1] , etc. (or axes[0][0] , axes[0][1] ... for a 2D grid). Each one behaves exactly like the single ax you've been using — just call .bar() , .hist() , .scatter() on whichever one you want. plt.tight_layout() is a small habit worth building now — it auto-adjusts spacing so titles and labels don't overlap. 5. Plotting Directly from a Pandas DataFrame The analyst's real workflow In practice, you're rarely typing out lists by hand — your data lives in a DataFrame. Pandas has a built-in .plot() method that calls Matplotlib for you, so you can skip a lot of boilerplate. The code import pandas as pd import matplotlib.pyplot as plt data = { " Month " : [ " Jan " , " Feb " , " Mar " , " Apr " , " May " ], " Revenue " : [ 45 , 50 , 47 , 53 , 60 ], " Expenses " : [ 30 , 32 , 31 , 35 , 38 ] } df = pd . DataFrame ( data ) # Quick line plot straight from the DataFrame df . plot ( x = " Month " , y = [ " Revenue " , " Expenses " ], kind = " line " , figsize = ( 8 , 5 ), title = " Revenue vs Expenses " ) plt . ylabel ( " Amount ($K) " ) plt . show () Other kind values worth knowing df . plot ( x = " Month " , y = " Revenue " , kind = " bar " , figsize = ( 8 , 5 ), color = " seagreen " ) plt . show () kind accepts "line" , "bar" , "barh" , "hist" , "scatter" , and more — one method, most of the charts you already learned today. Key insight: df.plot() still returns a Matplotlib Axes object under the hood, which is why plt.ylabel() , plt.title() , etc. still work on it afterward. Everything you learned on Day 1 still applies. 6. Hands-On: From CSV to Chart Here's a realistic mini-workflow — read tabular data and produce a combo chart: import pandas as pd import matplotlib.pyplot as plt # In real use: df = pd.read_csv("sales_data.csv")
Matplotlib - Session 2
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