NumPy has an extensive list of methods to generate random arrays and single numbers, or to randomly shuffle arrays. Continue reading “74. Using NumPy to generate random numbers, or shuffle arrays”
The function below may be used to set both the number of decimal places and the fixed width of NumPy print out. If no width is given it defaults to zero (no extra padding added). Setting width will ensure alignment of output. Continue reading “64. NumPy: Setting width and number of decimal places in NumPy print output”
Matplotlib has an easy method for plotting data. NumPy has an easy method for obtaining histogram data.
Plotting histograms with Matplotlib
Plotting a histogram with a defined number of bins: Continue reading “41. Matplotlib: Histograms (and obtaining histogram data with NumPy)”
Both NumPy and Pandas offer easy ways of removing duplicate rows. Pandas offers a more powerful approach if you wish to remove rows that are partly duplicated.
With numpy we use np.unique() to remove duplicate rows or columns (use the argument axis=0 for unique rows or axis=1 for unique columns). Continue reading “40. Removing duplicate data in NumPy and Pandas”
NumPy allows easy standard mathematics to be performed on arrays, a well as moire complex linear algebra such as array multiplication.
Lets begin by building a couple of arrays. We’ll use the np.arange method to create an array of numbers in range 1 to 12, and then reshape the array into a 3 x 4 array. Continue reading “35. Array maths in NumPy”
Using apply_along_axis (NumPy) or apply (Pandas) is a more Pythonic way of iterating through data in NumPy and Pandas (see related tutorial here). But there may be occasions you wish to simply work your way through rows or columns in NumPy and Pandas. Here is how it is done. Continue reading “34. Iterating through columns and rows in NumPy and Pandas”
A very powerful feature in Pandas is to use groupby to create groups of data. Each group may then be further acted on as if it were an independent dataframe. This allows for very sophisticated operations broken down by group.
Here we will create a very simple example to illustrate this. Continue reading “33. Subgrouping data in Pandas with groupby”