Python for Data Science

For a standard Python tutorial go to Python

Anaconda

Anaconda is a free and open source distribution of the Python and R programming languages for data science and machine learning related applications (large-scale data processing, predictive analytics, scientific computing), that aims to simplify package management and deployment. Package versions are managed by the package management system conda. https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)

En otras palabras, Anaconda puede ser visto como un paquete (a distribution) que incluye no solo Python (or R) but many libraries that are used in Data Science, as well as its own virtual environment system. It's an "all-in-one" install that is extremely popular in data science and Machine Learning.Creating sample array for the following examples:

Installation

https://linuxize.com/post/how-to-install-anaconda-on-ubuntu-18-04/

https://www.digitalocean.com/community/tutorials/how-to-install-the-anaconda-python-distribution-on-ubuntu-18-04

Anaconda comes with a few IDE

Jupyter Lab
Jupyter Notebook
Spyder
Qtconsole
and others

Anaconda Navigator

Anaconda Navigator is a GUI that helps you to easily start important applications and manage the packages in your local Anaconda installation

You can open the Anaconda Navigator from the Terminal:

anaconda-navigator

Jupyter

Jupyter comes with Anaconda.

It is a development environment (IDE) where we can write codes; but it also allows us to display images, and write down markdown notes.

It is the most popular IDE in data science for exploring and analyzing data.

Other famoues IDE for Python are Sublime Text and PyCharm.

There is Jupyter Lab and Jupyter Notebook

Online Jupyter

There are many sites that provides solutions to run your Jupyter Notebook in the cloud: https://www.dataschool.io/cloud-services-for-jupyter-notebook/

I have tried:

https://cocalc.com/app

https://cocalc.com/projects/595bf475-61a7-47fa-af69-ba804c3f23f9/files/?session=default

Parece bueno, pero tiene opciones que no son gratis

https://www.kaggle.com/

https://www.kaggle.com/adeloaleman/kernel1917a91630/edit

Parece bueno pero no encontré la forma adicionar una TOC

https://drive.google.com

https://colab.research.google.com

Es el que estoy utilizando ahora

Courses

Udemy - Python for Data Science and Machine Learning Bootcamp

https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

Most popular Python Data Science Libraries=

NumPy
SciPy
Pandas
Seaborn
SciKit'Learn
MatplotLib
Plotly
PySpartk

NumPy

NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.

Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great [StackOverflow post](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).

Installation

It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install.

If you have Anaconda, install NumPy by:

conda install numpy

If you are not using Anaconda distribution:

pip install numpy

Then, to use it:

import numpy as np
arr = np.arange(0,10)

Arrays

		Method/Operation		Description/Comments	Example
		Method/Operation		Description/Comments	import numpy as np
Methods for creating NumPy Arrays	From a Python List	`array()`		We can create an array by directly converting a list or list of lists.	`my_list = [1,2,3]` `np.array(my_list)` `my_matrix = [[1,2,3],[4,5,6],[7,8,9]]` `np.array(my_matrix)`
	From Built-in NumPy Methods	`arange()`		Return evenly spaced values within a given interval.	`np.arange(0,10)` `np.arange(0,11,2)`
		`zeros()`		Generate arrays of zeros.	`np.zeros(3)` `np.zeros((5,5))`
		`ones()`		Generate arrays of ones.	`np.ones(3)` `np.ones((3,3))`
		`linspace()`		Return evenly spaced numbers over a specified interval.	`np.linspace(0,10,3)` `np.linspace(0,10,50)`
		`eye()`		Creates an identity matrix.	`np.linspace(0,10,50)`
		`random`	`rand()`	Create an array of the given shape and populate it with random samples from a uniform distribution over `[0, 1)`.	np.random.rand(2) np.random.rand(5,5) # Another way to invoke a function: from numpy.random import rand # Then you can call the function directly rand(5,5)
			`randn()`	Return a sample (or samples) from the "standard normal" distribution. Unlike rand which is uniform.	`np.random.randn(2)` `np.random.randn(5,5)`
			`randint()`	Return random integers from `low` (inclusive) to `high` (exclusive).	`np.random.randint(1,100)` `np.random.randint(1,100,10)`
			`seed()`	sets the random seed of the NumPy pseudo-random number generator. It provides an essential input that enables NumPy to generate pseudo-random numbers for random processes. See s1 and s2. for explanation.	`np.random.seed(101)`
Others Array Attributes and Methods		`reshape()`		Returns an array containing the same data with a new shape.	`arr.reshape(5,5)`
		`max()`, `min()`, `argmax()`, `argmin()`		Finding max or min values. Or to find their index locations using argmin or argmax.	`arr.max()` `arr.argmax()`
		`shape()`		Shape is an attribute that arrays have (not a method).	NO LO ENTENDI.. REVISAR! #Length of array arr_length = arr2d.shape[1]
		`dtype()`		You can also grab the data type of the object in the array.	`arr.dtype`
-	-	-		-	-
Indexing and Selection How to select elements or groups of elements from an array. The general format is arr_2d[row][col] or arr_2d[row,col]. I recommend usually using the comma notation for clarity.				Creating sample array for the following examples: import numpy as np arr = np.arange(0,10) # 1D Array: arr = np.arange(0,11) #Show arr Output: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # 2D Array arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45])) #Show arr_2d Output: array([[ 5, 10, 15], [20, 25, 30], [35, 40, 45]])
	Bracket Indexing and Selection (Slicing)			Note: When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems! To get a copy, need to use the method copy(). See important note below.	#Get a value at an index arr[8] #Get values in a range arr[1:5] slice_of_arr = arr[0:6] #2D arr_2d[1] arr_2d[1][0] arr_2d[1,0] # The same that above #Shape (2,2) from top right corner arr_2d[:2,1:] #Output: array([[10, 15], [25, 30]]) #Shape bottom row arr_2d[2,:]
	Bracket Indexing and Selection (Slicing)			Fancy Indexing: Fancy indexing allows you to select entire rows or columns out of order. Example: # Set up matrix arr2d = np.zeros((10,10)) # Length of array arr_length = arr2d.shape[1] # Set up array for i in range(arr_length): arr2d[i] = i arr2d # Output: array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.], [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.], [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.], [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.], [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.], [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.], [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.], [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]]) # Fancy indexing allows the following arr2d[[6,4,2,7]] # Output: array([[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.], [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.], [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.], [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.]])
	Broadcasting (Setting a value with index range)			Setting a value with index range: Numpy arrays differ from a normal Python list because of their ability to broadcast.	arr[0:5]=100 #Show arr Output: array([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])
	Broadcasting (Setting a value with index range)				#Setting all the values of an Array arr[:]=99
	Get a copy of an Array	`copy()`		Note: When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems! To get a copy, need to use the method copy(). See important note below.	arr_copy = arr.copy()
	Important notes on Slices			slice_of_arr = arr[0:6] #Show slice slice_of_arr Output: array([0, 1, 2, 3, 4, 5]) #Making changes in slice_of_arr slice_of_arr[:]=99 #Show slice slice_of_arr Output: array([99, 99, 99, 99, 99, 99]) #Now note the changes also occur in our original array! #Show arr Output: array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10]) #When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems! #To get a copy, need to use the method copy()
	Using brackets for selection based on comparison operators and booleans			arr = np.arange(1,11) arr > 4 # Output: array([False, False, False, False, True, True, True, True, True, True]) bool_arr = arr>4 bool_arr # Output: array([False, False, False, False, True, True, True, True, True, True]) arr[bool_arr] # Output: array([ 5, 6, 7, 8, 9, 10]) arr[arr>2] # Output: array([ 3, 4, 5, 6, 7, 8, 9, 10]) x = 2 arr[arr>x] # Output: array([ 3, 4, 5, 6, 7, 8, 9, 10])
-	-	-		-	-
Arithmetic operations		`arr + arr` `arr - arr` `arr * arr` `arr/arr` `1/arr` `arr**3`		Warning on division by zero, but not an error! `0/0 -> nan` `1/0 -> inf`	import numpy as np arr = np.arange(0,10) arr + arr # Output: array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]) arr**3 # Output: array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])
Universal Array Functions		`np.sqrt(arr)`		Taking Square Roots	np.sin(arr) # Output: array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 , -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849])
		`np.exp(arr)`		Calcualting exponential (e^)
		`np.max(arr)` same as `arr.max()`		Max
		`np.sin(arr)`		Sin
		`np.log(arr)`		Natural logarithm

Pandas

You can think of pandas as an extremely powerful version of Excel, with a lot more features. In this section of the course, you should go through the notebooks in this order:

Series

A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.

		Method/Operator	Description/Comments	Example
		Method/Operator	Description/Comments	import pandas as pd
Creating Pandas Series You can convert a `list`, `numpy array`, or `dictionary` to a Series.	From a List	`pd.Series(my_list)`	# Creating some test data: labels = ['a','b','c'] my_list = [10,20,30] arr = np.array([10,20,30]) d = {'a':10,'b':20,'c':30} pd.Series(data=my_list) pd.Series(my_list) pd.Series(arr) # Output: 0 10 1 20 2 30 dtype: int64 pd.Series(data=my_list,index=labels) pd.Series(my_list,labels) pd.Series(arr,labels) pd.Series(d) # Output: a 10 b 20 c 30 dtype: int64
	From a NumPy Array	`pd.Series(arr)`
	From a Dectionary	`pd.Series(d)`
Data in a Series			A pandas Series can hold a variety of object types. Even functions (although unlikely that you will use this) pd.Series(data=labels) # Output: 0 a 1 b 2 c dtype: object # Holding «functions» into a Series # Output: pd.Series([sum,print,len]) 0 <built-in function sum> 1 <built-in function print> 2 <built-in function len> dtype: object
Index in Series			The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary). ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan']) ser1 # Output: USA 1 Germany 2 USSR 3 Japan 4 dtype: int64 ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan']) ser1['USA'] # Output: 1 # Operations are then also done based off of index: ser1 + ser2 # Output: Germany 4.0 Italy NaN Japan 8.0 USA 2.0 USSR NaN dtype: float64

DataFrames

DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let's use pandas to explore this topic!

import pandas as pd
import numpy as np

from numpy.random import randn
np.random.seed(101)

df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())

df
# Output: 
           W           X           Y           Z
A   2.706850    0.628133    0.907969    0.503826
B   0.651118   -0.319318   -0.848077    0.605965
C  -2.018168    0.740122    0.528813   -0.589001
D   0.188695   -0.758872   -0.933237    0.955057
E   0.190794    1.978757    2.605967    0.683509

DataFrame Columns are just Series:

type(df['W'])
# Output: 
pandas.core.series.Series

		Method/ Operator	Description/Comments	Example
Selection and Indexing Let's learn the various methods to grab data from a DataFrame	Standard systax	`df['']`		# Pass a list of column names: df[['W','Z']] W Z A 2.706850 0.503826 B 0.651118 0.605965 C -2.018168 -0.589001 D 0.188695 0.955057 E 0.190794 0.683509
	SQL syntax (NOT RECOMMENDED!)	`df.W`
	Selecting Rows	`df.loc['']`		df.loc['A'] # Or select based off of position instead of label : df.iloc[2] # Output: W 2.706850 X 0.628133 Y 0.907969 Z 0.503826 Name: A, dtype: float64
	Selecting subset of rows and columns	`df.loc['','']`		df.loc['B','Y'] # Output: -0.84807698340363147 df.loc[['A','B'],['W','Y']] # Output: W Y A 2.706850 0.907969 B 0.651118 -0.848077
	Conditional Selection		An important feature of pandas is conditional selection using bracket notation, very similar to numpy: df # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 C -2.018168 0.740122 0.528813 -0.589001 D 0.188695 -0.758872 -0.933237 0.955057 E 0.190794 1.978757 2.605967 0.683509 df>0 # Output: W X Y Z A True True True True B True False False True C False True True False D True False False True E True True True True df[df>0] # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 NaN NaN 0.605965 C NaN 0.740122 0.528813 NaN D 0.188695 NaN NaN 0.955057 E 0.190794 1.978757 2.605967 0.683509 df[df['W']>0] # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 D 0.188695 -0.758872 -0.933237 0.955057 E 0.190794 1.978757 2.605967 0.683509 df[df['W']>0]['Y'] # Output: A 0.907969 B -0.848077 D -0.933237 E 2.605967 Name: Y, dtype: float64 df[df['W']>0][['Y','X']] # Output: Y X A 0.907969 0.628133 B -0.848077 -0.319318 D -0.933237 -0.758872 E 2.605967 1.978757 # For two conditions you can use \| and & with parenthesis: df[(df['W']>0) & (df['Y'] > 1)] # Output: W X Y Z E 0.190794 1.978757 2.605967 0.683509
Creating a new column				df['new'] = df['W'] + df['Y']
Removing Columns		`df.drop()`	df.drop('new',axis=1) # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 C -2.018168 0.740122 0.528813 -0.589001 D 0.188695 -0.758872 -0.933237 0.955057 E 0.190794 1.978757 2.605967 0.683509 # Not inplace unless specified! df # Output: W X Y Z new A 2.706850 0.628133 0.907969 0.503826 3.614819 B 0.651118 -0.319318 -0.848077 0.605965 -0.196959 C -2.018168 0.740122 0.528813 -0.589001 -1.489355 D 0.188695 -0.758872 -0.933237 0.955057 -0.744542 E 0.190794 1.978757 2.605967 0.683509 2.796762 df.drop('new',axis=1,inplace=True) df # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 C -2.018168 0.740122 0.528813 -0.589001 D 0.188695 -0.758872 -0.933237 0.955057 E 0.190794 1.978757 2.605967 0.683509 # Can also drop rows this way: df.drop('E',axis=0) # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 C -2.018168 0.740122 0.528813 -0.589001 D 0.188695 -0.758872 -0.933237 0.955057
Resetting the index	Reset to default (0,1...n index)	`df.reset_index()`	df # Output: W X Y Z A 2.706850 0.628133 0.907969 0.503826 B 0.651118 -0.319318 -0.848077 0.605965 C -2.018168 0.740122 0.528813 -0.589001 D 0.188695 -0.758872 -0.933237 0.955057 E 0.190794 1.978757 2.605967 0.683509 df.reset_index() # Output: index W X Y Z 0 A 2.706850 0.628133 0.907969 0.503826 1 B 0.651118 -0.319318 -0.848077 0.605965 2 C -2.018168 0.740122 0.528813 -0.589001 3 D 0.188695 -0.758872 -0.933237 0.955057 4 E 0.190794 1.978757 2.605967 0.683509
Resetting the index	Setting index to something else	`df.set_index('')`	newind = 'CA NY WY OR CO'.split() df['States'] = newind df # Output: W X Y Z States A 2.706850 0.628133 0.907969 0.503826 CA B 0.651118 -0.319318 -0.848077 0.605965 NY C -2.018168 0.740122 0.528813 -0.589001 WY D 0.188695 -0.758872 -0.933237 0.955057 OR E 0.190794 1.978757 2.605967 0.683509 CO df.set_index('States') # Output: W X Y Z States CA 2.706850 0.628133 0.907969 0.503826 NY 0.651118 -0.319318 -0.848077 0.605965 WY -2.018168 0.740122 0.528813 -0.589001 OR 0.188695 -0.758872 -0.933237 0.955057 CO 0.190794 1.978757 2.605967 0.683509 df # Output: W X Y Z States A 2.706850 0.628133 0.907969 0.503826 CA B 0.651118 -0.319318 -0.848077 0.605965 NY C -2.018168 0.740122 0.528813 -0.589001 WY D 0.188695 -0.758872 -0.933237 0.955057 OR E 0.190794 1.978757 2.605967 0.683509 CO # We net to add «inplace=True»: df.set_index('States',inplace=True) df # Output: W X Y Z States CA 2.706850 0.628133 0.907969 0.503826 NY 0.651118 -0.319318 -0.848077 0.605965 WY -2.018168 0.740122 0.528813 -0.589001 OR 0.188695 -0.758872 -0.933237 0.955057 CO 0.190794 1.978757 2.605967 0.683509
Multi-Indexed DataFrame	Creating a Multi-Indexed DataFrame		# Index Levels outside = ['G1','G1','G1','G2','G2','G2'] inside = [1,2,3,1,2,3] hier_index = list(zip(outside,inside)) hier_index = pd.MultiIndex.from_tuples(hier_index) hier_index # Output: MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]], labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]) df = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B']) df # Output: A B G1 1 0.153661 0.167638 2 -0.765930 0.962299 3 0.902826 -0.537909 G2 1 -1.549671 0.435253 2 1.259904 -0.447898 3 0.266207 0.412580
Multi-Indexed DataFrame	Multi-Index and Index Hierarchy		df.loc['G1'] # Output: A B 1 0.153661 0.167638 2 -0.765930 0.962299 3 0.902826 -0.537909 df.loc['G1'].loc[1] # Output: A 0.153661 B 0.167638 Name: 1, dtype: float64 df.index.names # Output: FrozenList([None, None]) df.index.names = ['Group','Num'] df # Output: A B Group Num G1 1 0.153661 0.167638 2 -0.765930 0.962299 3 0.902826 -0.537909 G2 1 -1.549671 0.435253 2 1.259904 -0.447898 3 0.266207 0.412580 df.xs('G1') # Output: A B Num 1 0.153661 0.167638 2 -0.765930 0.962299 3 0.902826 -0.537909 df.xs(['G1',1]) # Output: A 0.153661 B 0.167638 Name: (G1, 1), dtype: float64 df.xs(1,level='Num') # Output: A B Group G1 0.153661 0.167638 G2 -1.549671 0.435253

Missing Data

https://www.geeksforgeeks.org/python-pandas-dataframe-dropna/

Let's show a few convenient methods to deal with Missing Data in pandas.

dropna() method allows the user to analyze and drop Rows/Columns with Null values in different ways:

DataFrameName.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

fillna() allows to fill Null fields with a given value:

import numpy as np
import pandas as pd


df = pd.DataFrame({'A':[1,2,np.nan],
                   'B':[5,np.nan,np.nan],
                   'C':[1,2,3]})

df
# Output:
      A       B     C
0   1.0     5.0     1
1   2.0     NaN     2
2   NaN     NaN     3


'''By default, dropna() drop all the rows without Null values:'''
df.dropna()
df.dropna(axis=0) # Same as default
# Output:
      A       B     C
0   1.0     5.0     1


'''If we want to display all the columns without Null values:'''
df.dropna(axis=1)


'''If we want to display all the rows that have at least 2 non-null values:'''
df.dropna(thresh=2)
# Output:
      A       B     C
0   1.0     5.0     1
1   2.0     NaN     2


'''Columns with at least 3 non-null values:'''
df.dropna(thresh=3)
# Output:
      A       B     C
0   1.0     5.0     1


'''To fill null fields with a given value:'''
df.fillna(value='FILL VALUE')
# Output:
    A            B            C
0   1            5            1
1   2            FILL VALUE   2
2   FILL VALUE   FILL VALUE   3


'''But many times what we want to do is to replace these null fields with, for example, the «mean» of the columns. We can do it this way:'''
df['A'].fillna(value=df['A'].mean())
# Output:
0    1.0
1    2.0
2    1.5  # *
Name: A, dtype: float64

'''* The Null field has been filled with the mean of the column'''

GroupBy

The groupby method allows you to group rows of data together and call aggregate functions

Now you can use the .groupby() method to group rows together based on a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:

	Method	Description/Example
	import pandas as pd # Create dataframe data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'], 'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'], 'Sales':[200,120,340,124,243,350]} df = pd.DataFrame(data) df # Output: Company Person Sales 0 GOOG Sam 200 1 GOOG Charlie 120 2 MSFT Amy 340 3 MSFT Vanessa 124 4 FB Carl 243 5 FB Sarah 350
GroupBy	`df.groupby('')` This will create a DataFrameGroupBy object.	For instance let's group based off of Company: df.groupby('Company') # Output: <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f2027fdd470> '''You can save this object as a new variable:''' by_comp = df.groupby("Company") '''And then call aggregate methods off the object:'''
We can call aggregate methods on the `groupBy` object	`df.mean()df.groupby('Company').mean()`	df.groupby('Company').mean() by_comp.mean() # Output: Sales Company FB 296.5 GOOG 160.0 MSFT 232.0
	`df.std()df.groupby('Company').std()`	by_comp.std() # Output: Sales Company FB 75.660426 GOOG 56.568542 MSFT 152.735065
	`df.min()df.max()` `df.groupby('Company').min()`	by_comp.min() # Output: Person Sales Company FB Carl 243 GOOG Charlie 120 MSFT Amy 124 by_comp.max()
	`df.count()df.groupby('Company').count()`	by_comp.count() # Output: Person Sales Company FB 2 2 GOOG 2 2 MSFT 2 2
	`df.describe()df.groupby('Company').describe()`	by_comp.describe() # Output: Sales Company FB count 2.000000 mean 296.500000 std 75.660426 min 243.000000 25% 269.750000 50% 296.500000 75% 323.250000 max 350.000000 GOOG count 2.000000 mean 160.000000 std 56.568542 min 120.000000 25% 140.000000 50% 160.000000 75% 180.000000 max 200.000000 MSFT count 2.000000 mean 232.000000 std 152.735065 min 124.000000 25% 178.000000 50% 232.000000 75% 286.000000 max 340.000000 by_comp.describe().transpose() by_comp.describe().transpose()['GOOG'] # Output: count mean std min 25% 50% 75% max Sales 2.0 160.0 56.568542 120.0 140.0 160.0 180.0 200.0

Python for Data Science

Contents

Anaconda

Installation

Anaconda comes with a few IDE

Anaconda Navigator

Jupyter

Online Jupyter

Courses

Most popular Python Data Science Libraries=

NumPy

Installation

Arrays

Methods for creating NumPy Arrays

From a Python List

From Built-in NumPy Methods

Others Array Attributes and Methods

Indexing and Selection

Bracket Indexing and Selection (Slicing)

Broadcasting

Get a copy of an Array

Important notes on Slices

Using brackets for selection based on comparison operators and booleans

Arithmetic operations

Universal Array Functions

Pandas

Series

Creating Pandas Series

From a List

From a NumPy Array

From a Dectionary

Data in a Series

Index in Series

DataFrames

Selection and Indexing

Standard systax

SQL syntax

Selecting Rows

Selecting subset of rows and columns

Conditional Selection

Creating a new column

Removing Columns

Resetting the index

Reset to default

Setting index to something else

Multi-Indexed DataFrame

Creating a Multi-Indexed DataFrame

Multi-Index and Index Hierarchy

Missing Data

GroupBy

Merging,Joining,and Concatenating

Operations

Data Input and Output

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