Back to Cheatsheets

Table of Contents

1. GETTING STARTED

• Import
• Read CSV

2. ARRAYS

• Create
• Element-wise operations
• Combine
• 2D
• Select
  • Select from 1D
  • Select from 2D
  • Logical Operations

3. STATISTICS

• Spread
  • Variance
  • Standard Deviation
  • Range
  • Outliers
• Quantiles
  • Median
  • Percentiles
  • Quartiles

4. PLOTS

• Histogram
• Boxplot

GETTING STARTED

Import

import numpy as np

Read CSV

np.genfromtxt('file_name.csv', delimiter=',')

ARRAYS

Create

np.array([#1, #2, #3])
OR 
np.array(list_name)

• a special type of list (NumPy arrays are more efficient than lists)
• a data structure that organizes multiple items
• each item can be of any type (strings, numbers, or even other arrays).
• allow you to do element-wise operations

Element-wise operations

np_array_name + #

• quickly perform an operation, such as addition, on each element in an array.
• np_array ** 2 : square all
• np.sqrt(np_array) : square root of all

Combine

new_np_array = np_array_1 + np_array_2 + np_array_3

• arrays can be added to or subtracted from each other
• all arrays must have the same number of elements.
• each element will be added/subtracted to its matching element.

2D

np.array([np_array_1, 
         np_array_2,
         np_array_3])

• Two-dimensional array =create an array of arrays, a list of lists where each list has the same number of elements
• often use two-dimensional arrays to represent a set of samples

Select

Select from 1D

np_array_name[index]

• zero-indexed numbering, same indexing as regular python

Select from 2D

np_array_name[row,column]

• the relationship between the interior arrays is defined in terms of 2 axes.
• axis 0 : same column - the values that share the same indexical position
• axis 1 : same row - the values that share an array
• : : selects the entire row/column eg. a[:,0] selects all in first column

Logical Operations

array_name[array_name > 5]
array_name[(array_name > 5) | (array_name < 2)]

• checks to see whether statement evaluates to True for each item in the array, without having to use a for loop
• combine logical statements to further specify our criteria ()each statement in parentheses and use boolean operators)
• | : or
• & : and

STATISTICS

MEAN

Average

np.mean(survey_array)
OR
np.average(array_nums)

Logical statements

np.mean(array_name op #)

• eg. np.mean(survey_array > 8)
• calculate the percent of array elements that have a certain property (i.e. condition true)
• resulting mean value will be equivalent to the total number of True items divided by the total array length.

2D Arrays

np.mean(2D_array_name, axis=#)

• axis=1 : Find mean of each interior array (i.e. the rows)
• axis=0 : Find mean of each index position (i.e. the columns)
• np.mean(2D_array_name) : Find mean across all arrays
• calculate the means of the larger array as well as the interior values.

VARIATION

Variance

np.var(dataset)

Standard Deviation

np.std(dataset)

Range

np.amin(dataset)
np.amax(dataset)

• range = max - min

Outliers

np.sort(datset)

• One way to quickly identify outliers is by sorting our data, Once our data is sorted, we can quickly glance at the beginning or end of an array to see if some values lie far beyond the expected range

QUANTILES

np.quantile(dataset, [list of quantiles])

• points that split a dataset into groups of equal size.

Median

np.median(array_nums)

PERCENTILES

np.percentile(patrons, 30)

QUARTILES

np.quantile(dataset, Q#)
OR 
np.percentile(dataset, %#)

• Q# = number between 0 and 1
• %N = number between 1 and 100
• 0.5 or 50 = the second quartile (Q2), the median, 50th percentile, half below and half data above
• 0.25 or 25 = first quartile (Q1), 25th percentile
• 0.75 or 75= third quartile (Q3), 75th percentile

HISTOGRAM

np.histogram(array_name, range= (min, max), bins = #)

STATISTICAL DISTRIBUTIONS

Normal Distribution

np.random.normal(mean, std, size=#_of_random)

• symmetric, unimodal
• Normal distributions are defined by their mean and standard deviation. The mean sets the “middle” of the distribution, and the standard deviation sets the “width” of the distribution. A larger standard deviation leads to a wider distribution. A smaller standard deviation leads to a skinnier distribution. loc: the mean for the normal distribution scale: the standard deviation of the distribution size: the number of random numbers to generate