DataCamp PythonForDataScience

1 Pages·2018·0.207 MB·English

by  it-ebooks

Python For Data Science Cheat Sheet Lists Also see NumPy Arrays Libraries
Import libraries
Python Basics >>> a = 'is'
>>> b = 'nice' >>> import numpy Data analysis Machine learning
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>>> my_list = ['my', 'list', a, b] >>> import numpy as np
Selective import
>>> my_list2 = [[4,5,6,7], [3,4,5,6]]
>>> from math import pi Scientific computing 2D plotting
Variables and Data Types Selecting List Elements Index starts at 0
Install Python
Variable Assignment Subset
Select item at index 1
>>> my_list[1]
>>> x=5 Select 3rd last item
>>> my_list[-3]
>>> x Slice
5 Select items at index 1 and 2
>>> my_list[1:3]
Calculations With Variables Select items after index 0
>>> my_list[1:]
Sum of two variables >>> my_list[:3] Select items before index 3 Leading open data science platform Free IDE that is included Create and share
>>> x+2 Copy my_list powered by Python with Anaconda documents with live code,
>>> my_list[:]
7 Subset Lists of Lists visualizations, text, ...
Subtraction of two variables
>>> x-2 my_list[list][itemOfList]
>>> my_list2[1][0] Numpy Arrays Also see Lists
3
Multiplication of two variables >>> my_list2[1][:2]
>>> x*2
List Operations >>> my_list = [1, 2, 3, 4]
10
>>> x**2 Exponentiation of a variable >>> my_array = np.array(my_list)
25 >>> my_list + my_list >>> my_2darray = np.array([[1,2,3],[4,5,6]])
>>> x%2 Remainder of a variable ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice'] Selecting Numpy Array Elements Index starts at 0
1 >>> my_list * 2
Division of a variable Subset
>>> x/float(2) ['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']
Select item at index 1
2.5 >>> my_list2 > 4 >>> my_array[1]
Types and Type Conversion True S li2ce
List Methods
Select items at index 0 and 1
str() '5', '3.45', 'True' V ariables to strings >>> my_array[0:2]
>>> my_list.index(a) G e t th e in dex of an item array([1, 2])
Count an item Subset 2D Numpy arrays
int() 5, 3, 1 Variables to integers >>>>>> mmyy__lliisstt..caopupnetn(da()'!') Append an item at a time >>> my_2darray[:,0] my_2darray[rows, columns]
float() 5.0, 1.0 Variables to floats >>> my_list.remove('!') Remove an item array([1, 4])
Remove an item Numpy Array Operations
>>> del(my_list[0:1])
Reverse the list
bool() True, True, True Variables to booleans >>> my_list.reverse() >>> my_array > 3
>>> my_list.extend('!') Append an item array([False, False, False, True], dtype=bool)
Asking For Help >>> my_list.pop(-1) Remove an item >>> my_array * 2
>>> my_list.insert(0,'!') Insert an item
array([2, 4, 6, 8])
>>> help(str) >>> my_list.sort() Sort the list >>> my_array + np.array([5, 6, 7, 8])
Strings array([6, 8, 10, 12])
Numpy Array Functions
>>> my_string = 'thisStringIsAwesome' String Operations Index starts at 0
>>> my_string >>> my_array.shape Get the dimensions of the array
'thisStringIsAwesome' >>> my_string[3] >>> np.append(other_array) Append items to an array
>>> my_string[4:9] >>> np.insert(my_array, 1, 5) Insert items in an array
String Operations
String Methods >>> np.delete(my_array,[1]) Delete items in an array
Mean of the array
>>> np.mean(my_array)
>>> my_string * 2
'thisStringIsAwesomethisStringIsAwesome' >>> my_string.upper() String to uppercase >>> np.median(my_array) Median of the array
>>> my_string + 'Innit' >>> my_string.lower() String to lowercase >>> my_array.corrcoef() Correlation coefficient
'thisStringIsAwesomeInnit' >>> my_string.count('w') Count String elements >>> np.std(my_array) Standard deviation
Replace String elements
>>> 'm' in my_string >>> my_string.replace('e', 'i')
True >>> my_string.strip() Strip whitespaces DataCamp
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