Welcome to PythonA Level-1 Heading
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Anaconda Navigator landing page
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Anaconda Navigator landing page
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JupyterLab Menu Bar
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JupyterLab Left Side Bar
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JupyterLab Main Work Area
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Example Jupyter Notebook
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Multi-panel JupyterLab
Variables in Python
Basic Types
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Python uses 0-based indexing.
Built-in Functions and Help
String Manipulation
Using Objects
Lists
Figure 1
veg is represented as a shelf full
of produce. There are three rows of vegetables on the shelf, and each
row contains three baskets of vegetables. We can label each basket
according to the type of vegetable it contains, so the top row contains
(from left to right) lettuce, lettuce, and peppers.Figure 2
![veg is now shown as a list of three rows, with veg[0] representing the top row of three baskets, veg[1] representing the second row, and veg[2] representing the bottom row.](fig/05_groceries_veg0.png)
veg is now shown as a list of three
rows, with veg[0] representing the top row of three
baskets, veg[1] representing the second row, and
veg[2] representing the bottom row.Figure 3
To reference a specific basket on a specific shelf, you use two
indexes. The first index represents the row (from top to bottom) and the
second index represents the specific basket (from left to right). ![veg is now shown as a two-dimensional grid, with each basket labeled according to its index in the nested list. The first index is the row number and the second index is the basket number, so veg[1][3] represents the basket on the far right side of the second row (basket 4 on row 2): zucchini](fig/05_groceries_veg00.png)
For Loops
Libraries
Reading tabular data
Managing Python Environments
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Changing the kernel
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Selecting conda kernels in Jupyer Lab
Dictionaries
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Illustrative diagram of associative arrays,
showing the sets of keys and their association with some of the
values.
Conditionals
Pandas DataFrames
Writing Functions
Perform Statistical Tests with Scipy
Reshaping Data
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Melt goes from wide to long data
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Pivot_table goes from long to wide
Combining Data
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A summary of the types of joins and what they
keep and drop.
Visualizing data with matplotlib and seaborn
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Simple Position-Time Plot
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Expression histogram
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Boxplot by timepoint
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Histogram by timpoint as subplots.
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Asl expression over time
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Multiple lines on the same plot with a
legend
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A simple boxplot using seaborn
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Basic foldchange scatterplot.
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Axis labels changed
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Adjusted alpha level
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Adjusted point size
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Added gene_biotype as hue
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Style set to is_protein_coding
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Adding chromosome
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Cleaning up the plot
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Default clustered heatmap
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Using magma colormap
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Heatmap with infection information
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Perform machine learning with Scikit-learn
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Supervised Machine Learning Workflow
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Holdout validation strategy
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ROC curve
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PR curve
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Confusion Matrix