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Python Data Visualization with NumPy and pandas

Arrays with NumPy, tables with pandas and charts with matplotlib, the toolkit every machine learning project starts with. Write the code, and your chart appears in the Plots tab.

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60 lessons59 challenges250 quiz questions

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Syllabus

1 sections60 lessons59 challenges250 quiz questions

This section is part of the Machine Learning Journey. The full syllabus has more sections - click any preview below to view it on the Journey page.

  1. Section 1

    Data and Plots

    Start sectionStartExpandCollapse    The toolkit every machine learning project starts with: NumPy arrays, matplotlib charts and pandas tables. Load real datasets, summarize them and plot what you find.60 lessons59 challenges250 questions

    Introduction

    4 lessons320

    NumPy Arrays

    5 lessons520

    Array Math

    6 lessons625

    Shaping Data

    4 lessons415

    Line and Scatter Plots

    6 lessons625

    Charts for Data

    5 lessons520

    pandas DataFrames

    6 lessons625

    Filtering and Sorting

    5 lessons520

    Grouping and Cleaning

    5 lessons520

    Plotting DataFrames

    5 lessons520

    Datasets for ML

    5 lessons520

    Final Challenges

    4 lessons420
  2. Section 2Statistics for ML64 lessons
  3. Section 3Supervised Learning58 lessons
  4. Section 4Unsupervised Learning51 lessons
View the full Machine Learning Journey syllabus
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Intro to Variables
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A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

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Why learn NumPy, pandas and matplotlib with Coddy

  • Your chart, right away. Type plt.plot(x, y), press Run and the real matplotlib figure appears in the Plots tab next to your code. Every lesson has a chart or a diagram, from an array drawn as numbered cells to a groupby sliding rows into piles.
  • NumPy the way ML uses it: arrays and shapes, slicing, math on whole arrays, statistics by axis, broadcasting, boolean masks and seeded random numbers.
  • matplotlib from the first line: line and scatter plots, labels and legends, colours and markers, bar charts, histograms, side-by-side charts with plt.subplots, and arrays shown as images.
  • pandas on real tables: reading CSV files, selecting, filtering, sorting, new columns, value_counts, groupby, missing values, and plotting straight from a DataFrame.

Frequently asked questions about NumPy, pandas and matplotlib

What is the difference between NumPy, pandas and matplotlib?

NumPy holds numbers in fast arrays and does math on all of them at once. pandas builds tables on top of NumPy, with named columns, CSV files, filtering and grouping. matplotlib draws the charts. Almost every data science or machine learning program in Python imports all three, which is why this section teaches them together.

Should I learn NumPy or pandas first?

NumPy, briefly, then pandas. A pandas column is a NumPy array with a name, so masks, math on whole columns and statistics by axis make sense once you have seen them on arrays. The section spends four chapters on NumPy, then moves to pandas with that already in hand, and draws charts all the way through.

Do I need to install anything to make charts?

No. Your code runs on Coddy with real matplotlib, and the figure it draws comes back to the Plots tab in your browser. There is no Jupyter notebook or Anaconda to set up, so a Chromebook or a work laptop without admin rights is fine.

Which charts will I learn to make?

Line charts, scatter plots, several lines with a legend, bar charts, histograms, several charts in one figure, images from arrays, charts straight from a DataFrame, one colour per group, and comparing two groups' histograms. Each one titled and labelled so it could go in a report.

Do I need to know Python before NumPy and pandas?

The basics: variables, loops, functions and lists, the Fundamentals section of the Python course. Dictionaries, import and anything else the section needs are explained in one sentence where they first appear.

How long does it take to learn NumPy, pandas and matplotlib?

This section is 60 lessons, about 10 hours at your own pace: three weeks at half an hour a day. It is the first section of the machine learning course, so when you finish, models are the next step.

Is this NumPy, pandas and matplotlib course free?

Yes. The lessons, the charts, the AI hints and the section certificate are free. A free account works through a few lessons a day, and Pro removes the daily limit.

Learning paths that include this course

This course is one step of a longer path: several courses in the order a mentor would teach them, each with its own free certificate.

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