NumPy Cheat Sheet PDF
Download our NumPy cheat sheet for quick access to essential array creation, reshaping, and key operations for efficient data analysis.
NumPy Cheat Sheet PDF
Download our NumPy cheat sheet for quick access to essential array creation, reshaping, and key operations for efficient data analysis.
Importing Data
Syntax for
How to use
Explained
IMPORT
import numpy as npImports NumPy using its standard alias, np
LOADTXT
np.loadtxt('file.txt')Create an array from a .txt file
GENFROMTXT
np.genfromtxt('file.csv', delimiter=',')Create an array from a .csv file
SAVETXT
np.savetxt('file.txt', arr, delimiter=' ')Writes an array to a .txt file
np.savetxt('file.csv', arr, delimiter=',')Writes an array to a .csv file
Creating Arrays
Syntax for
How to use
Explained
ARRAY
arr = np.array([1, 2, 3])Create a 1D array
arr = np.array([(1, 2, 3), (4, 5, 6)])Create a 2D array
ZEROS
arr = np.zeros(3)1D array of length 3; all values set to 0
ONES
arr = np.ones((3, 4))3x4 array with all values set to 1
EYE
arr = np.eye(5)5x5 array of 0 with 1 on diagonal (identity matrix)
LINSPACE
arr = np.linspace(0, 100, 6)Array of 6 evenly divided values from 0 to 100 ([0, 20, 40, 60, 80, 100])
ARRANGE
arr = np.arange(0, 10, 3)Array of values from 0 to less than 10 with step 3 ([0, 3, 6, 9])
FULL
arr = np.full((2, 3), 8)2x3 array with all values set to 8
RAND
arr = np.random.rand(4, 5)4x5 array of random floats between 0 and 1
arr = np.random.rand(6, 7) * 1006x7 array of random floats between 0-100
RANDINT
arr = np.random.randint(5, size=(2, 3))2x3 array with random integers between 0 and 4
Inspecting Properties
Syntax for
How to use
Explained
ASTYPE
arr.astype(dtype)Convert arr elements to type dtype
TOLIST
arr.tolist()Convert arr to a Python list
INFO
np.info(np.eye) View documentation for np.eye
SIZE
arr.sizeReturns number of elements in arr
SHAPE
arr.shapeReturns dimensions of arr (rows, columns)
DTYPE
arr.dtypeReturns type of elements in arr
Copying, Sorting, & Reshaping
Syntax for
How to use
Explained
COPY
np.copy(arr)Copies arr to new memory
VIEW
arr.view(dtype)Creates view of arr elements with type dtype
SORT
arr.sort()Sorts arr
SORT
arr.sort(axis=0)Sorts specific axis of arr
FLATTEN
two_d_arr.flatten()Flattens 2D array two_d_arr to 1D
T
arr.TTransposes arr (rows become columns and vice versa)
RESHAPE
arr.reshape(3, 4)Reshapes arr to 3 rows, 4 columns without changing data
RESIZE
arr.resize((5, 6))Changes arr shape to 5x6 and fills new values with 0
Adding & Removing Elements
Syntax for
How to use
Explained
APPEND
np.append(arr, values)Appends values to end of arr
INSERT
np.insert(arr, 2, values)Inserts values into arr before index 2
DELETE
np.delete(arr, 3, axis=0)Deletes row on index 3 of arr
np.delete(arr, 4, axis=1)Removes the 5th column from arr
Combining & Splitting
Syntax for
How to use
Explained
CONCATENATE
np.concatenate((arr1, arr2), axis=0)Adds arr2 as rows to the end of arr1
np.concatenate((arr1, arr2), axis=1)Adds arr2 as columns to the end of arr1
SPLIT
np.split(arr, 3)Splits arr into 3 sub-arrays
HSPLIT
np.hsplit(arr, 5)Splits arr horizontally on the index 5
Indexing & Slicing
Syntax for
How to use
Explained
INDEXING
arr[5]Returns the element at index 5
arr[2, 5]Returns the 2D array element on index [2][5]
arr[1] = 4Assigns array element on index 1 the value 4
arr[1, 3] = 10Assigns array element on index [1][3] the value 10
SLICING
arr[0:3]Returns the elements at indices 0, 1, 2
arr[0:3, 4]Returns the elements on rows 0, 1, 2 in column index 4
arr[:2]Returns the elements at indices 0, 1
arr[:, 1]Returns column index 1, all rows
CONDITIONAL STATEMENTS
arr < 5Returns an array of boolean values
(arr1 < 3) & (arr2 > 5)To be True, both must be True
~arrInverts a boolean array
arr[arr < 5]Returns array elements less than 5
(arr1 < 3) | (arr2 > 5) To be True, at least one must be True
Scalar Math
Syntax for
How to use
Explained
ADD
np.add(arr, 1)Add 1 to each array element
SUBTRACT
np.subtract(arr, 2)Subtract 2 from each array element
MULTIPLY
np.multiply(arr, 3)Multiply each array element by 3
DIVIDE
np.divide(arr, 4)Divide each array element by 4 (returns np.nan for division by zero)
POWER
np.power(arr, 5)Raise each array element to the power of 5
Vector Math
Syntax for
How to use
Explained
ADD
np.add(arr1, arr2)Elementwise add arr1 to arr2
SUBTRACT
np.subtract(arr1, arr2)Elementwise subtract arr2 from arr1
MULTIPLY
np.multiply(arr1, arr2)Elementwise multiply arr1 by arr2
DIVIDE
np.divide(arr1, arr2)Elementwise divide arr1 by arr2
POWER
np.power(arr1, arr2)Elementwise, raise arr1 to the power of arr2
ARRAY_EQUAL
np.array_equal(arr1, arr2)Returns True if the arrays have the same elements and shape
SQRT
np.sqrt(arr)Square root of each element in the array
SIN
np.sin(arr)Sine of each element in the array
LOG
np.log(arr)Natural log of each element in the array
ABS
np.abs(arr)Absolute value of each element in the array
CEIL
np.ceil(arr)Rounds up each element to the nearest integer
FLOOR
np.floor(arr)Rounds down each element to the nearest integer
ROUND
np.round(arr)Rounds each element to the nearest integer
Statistics
Syntax for
How to use
Explained
MEAN
np.mean(arr, axis=0)Returns mean of arr along specified axis
SUM
arr.sum()Returns the sum of elements in arr
MIN
arr.min()Returns minimum value of arr
MAX
arr.max(axis=0)Returns maximum value of arr along specified axis
VAR
np.var(arr)Returns the variance of arr
STD
np.std(arr, axis=1)Returns the standard deviation of arr along specified axis
CORRCOEF
arr.corrcoef()Returns correlation coefficient of arr
Working with Data
Syntax for
How to use
Explained
CREATING NDARRAYS
import numpy as np
array_1d = np.array([1, 2, 3, 4, 5])
array_2d = np.array([[1, 2, 3], [4, 5, 6]])Create a 1D or 2D ndarray
CONVERTING A LIST OF LISTS
import csv
f = open("nyc_taxis.csv", "r")
taxi_list = list(csv.reader(f))
taxi = np.array(taxi_list)Convert a list of lists into a 2D ndarray
SELECTING ROWS
second_row = taxi[1]Select the second row in taxi
all_but_first_row = taxi[1:]Select all rows from the second row onward in taxi
fifth_row_second_column = taxi[4, 1]Select the element from the fifth row and second column in taxi
SELECTING COLUMNS
second_column = taxi[:, 1]Select all values from the second column in taxi
second_third_columns = taxi[:, 1:3]
cols = [1, 3, 5]
second_fourth_sixth_columns = taxi[:, cols]Select the second and third columns, then the second, fourth, and sixth columns in taxi
twod_slice = taxi[1:4, :3]Select a slice of rows 2 to 4 and columns 1 to 3 in taxi
VECTOR OPERATIONS
vector_a + vector_bElement-wise addition of two ndarray objects
vector_a - vector_bElement-wise subtraction of two ndarray objects
vector_a * vector_bElement-wise multiplication of two ndarray objects
vector_a / vector_bElement-wise division of two ndarray objects
STATISTICS FOR 1D NDARRAYS
array_1d.min()Return the minimum value of array_1d
array_1d.max()Return the maximum value of array_1d
array_1d.mean()Calculate the average of values in array_1d
array_1d.sum()Calculate the sum of the values in array_1d
STATISTICS FOR 2D NDARRAYS
array_2d.max()Return the maximum value for the entire array_2d
array_2d.max(axis=1) # returns a 1D ndarrayReturn the maximum value in each row in array_2d
array_2d.max(axis=0) # returns a 1D ndarrayReturn the maximum value in each column in array_2d
CREATING AN NDARRAY FROM CSV FILE
import numpy as np
taxi = np.genfromtxt('nyc_taxis.csv', delimiter=',', skip_header=1)Load data from the nyc_taxis.csv file into an ndarray, skipping the header row
WORKING WITH BOOLEAN ARRAYS
np.array([2, 4, 6, 8]) < 5Create a Boolean array for elements less than 5
a = np.array([2, 4, 6, 8])
filter = a < 5
a[filter] # returns [2, 4]Use Boolean filtering to return elements less than 5 from an ndarray
tip_amount = taxi[:, 12]
tip_bool = tip_amount > 50
top_tips = taxi[tip_bool, 5:14]Use Boolean filtering to return rows with tip_amount > 50 and columns 6 to 14
ASSIGNING NDARRAY VALUES
taxi[1066, 5] = 1
taxi[:, 0] = 16
taxi[550:552, 7] = taxi[:, 7].mean()Assign values to specific elements, a column, and a slice in taxi
taxi[taxi[:, 5] == 2, 15] = 1Use Boolean indexing to assign a value of 1 in column index 15 to rows where the 6th column equals 2
Written by
Mike Levy
Mike is a life-long learner who is passionate about mathematics, coding, and teaching. When he's not sitting at the keyboard, he can be found in his garden or at a natural hot spring.
Join 1M+ data learners on Dataquest.
- 1
Create a free account
- 2
Choose a learning path
- 3
Complete exercises and projects
- 4
Advance your career