Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts
Tuesday, April 16, 2019
Wednesday, October 17, 2018
Python for Data Science - Basics, Descriptive Statistics, Hypothesis Testing,& Regression Models
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
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mtcars=pd.read_csv("/Users/Rajesh Prabhakar/Desktop/Datasets/mtcars.csv")
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# The first commands to run on data import:
# dfname.head() First 5 rows of data
# dfname.tail() Last t5 rows of data
# dfname.shape First no of rows & Second no of columns
# dfname.dtypes Individual Data Type of each variable
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mtcars.head()
#head() # Function predefined & Paranthesis or brackets round must
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mtcars.tail() # last five rows of dataframe
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mtcars.shape # Number of rows and Number of columns
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mtcars.dtypes
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# The following Big Data Data Types:
# a) Numerical - Integer & Float
# b) String or Character or Text (" ")
# c) Boolean or Logical (True,False)
# d) Images - jpeg, png, tiff, satellite,
# e) Geo Spatial - latitude, longitudes, GPS, maps
# f) Internet of Things (Sensor based Data like Temperature)
# g) Social Media - Updates, Videos, Pictures, Tweets, Networks
# h) High Frequency Data - very fast updating stock prices, currency exchange rates
# i) Streaming Data - Tweets, Videos, Pics, Traffic
# j) Traffic Data - Web or Road Traffic
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# BIG DATA ANALYTICS - DESCRIPTIVE ANALYTICS - UNIVARIATE STATISTICS
# MEASURES OF CENTRAL TENDENCY - MEAN, MEDIAN, MODE
# MEASURES OF DISPERSION - RANGE, VARIANCE, STANDARD DEVIATION, QUARTILES, PERCENTILES
# MEASURES OF ASSYMMETRY - SKEWNESS & KURTOSIS
# MEASURES OF RELATIONSHIP - COVARIANCE & CORRELATION (BIVARIATE STATISTICS)
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mtcars.describe().transpose()
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mtcars.var() # Variance
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mtcars.skew()
# Positive Skewness Correction - Logarthmic or Square
# Negative Skewness Correction - Exponential or Power
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mtcars.kurt() # kurtosis
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mtcars.cov() # Covariance
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mtcars.corr() # Correlation
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# DATA VISUALIZATIONS:UNIVARIATE
# LINE PLOTS
# COLUMN BARS - VERTICAL & HORIZONTAL, STACKED
# PIE CHARTS
# ADVANCED VISUALIZATIONS FOR DATA SCIENCE:
# HISTOGRAM - Detect SKEWNESS, KURTOSIS, MISSING VALUES & OUTLIERS
# BOXPLOT - Detect OUTLIERS, SKEWNESS, KURTOSIS, MISSING VALUES
# DENSITY CURVES - Detect SKEWNESS, KURTOSIS, MISSING VALUES & OUTLIERS
# THE ABOVE THREE GRAPHS MUST FOR TARGET VARIABLE IF IT IS NUMERICAL & CONTINOUS
# OUTLIERS ARE EXTREME VALUES THAT FALL OUTSIDE THE NORMAL RANGE
# LOWER OUTLIERS - Q1 - 1.5*INTER QUARTILE RANGE (Q3-Q1)
# UPPER OUTLIERS - Q3 + 1.5*INTER QUARTILE RANGE (Q3-Q1)
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mtcars.mpg.describe() # Descriptive Statistics of mileage per gallon variable
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mtcars[['mpg','hp','wt']].describe() # Descriptive statistics of 3 columns
# Double Square brackets for selection of multiple variables
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mtcars.plot(kind='scatter',x='mpg',y='drat')
# kind= 'hist' or 'line' or 'bar' or 'box' or 'density' or 'pie' or 'scatter'
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In [21]:
plt.subplot(211)
mtcars.mpg.plot(kind='box',vert=False)
plt.subplot(212)
mtcars.mpg.plot(kind='density')
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In [22]:
# DATA MANIPULATION
# DO DESCRIBE OF MPG COLUMN
# CREATE A NEW VARIABLE "MILEAGETYPE" BASED ON MPG - HIGHMILEAGE(>22.80),
# MEDIUMMILEAGE(>19.20 & <22.80) ,& lOW MILEAGE (<19.20)
In [23]:
mtcars.mpg.describe()
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mileagetype=[]
for row in mtcars.mpg:
if row>=22.80:
mileagetype.append("Highmileage")
elif row>=19.20:
mileagetype.append("Mediummileage")
else:
mileagetype.append("Lowmileage")
mtcars['mileagetype']=mileagetype
In [25]:
pd.value_counts(mtcars.mileagetype) # frequency counts of newly created variable
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# CREATE A NEW VARIABLE "SPEEDTYPE" BASED ON "HP" - HIGH, MEDIUM & LOW SPEED
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speedtype=[]
for row in mtcars.hp:
if row>=180:
speedtype.append("highspeed")
elif row>=123:
speedtype.append("mediumspeed")
else:
speedtype.append("lowspeed")
mtcars['speedtype']=speedtype
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pd.value_counts(mtcars.speedtype)
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weighttype=[]
for row in mtcars.wt:
if row>=3.61:
weighttype.append("heavywt")
elif row>=3.32:
weighttype.append("mediumwt")
else:
weighttype.append("lowwt")
mtcars['weighttype']=weighttype
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pd.value_counts(mtcars.weighttype)
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pd.value_counts(mtcars.am)
# 0 means Automatic
# 1 means Manual
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# groupby function is a pivot function
# Is the Average mileage of Automatic & Manual Cars is Same or Equal
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mtcars.mpg.groupby(mtcars.am).mean()
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mtcars.mpg.groupby(mtcars.am).var()
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# NULL - THERE IS NO SIGNIFICANT DIFFERENCE IN AVERAGE MPG OF AUTOMATIC &
# MANUAL CARS - BOTH MEANS ARE EQUAL
# ALTERNATE - THERE IS SIGNIFICANT DIFFERENCE IN AVERAGE MPG OF AUTOMATIC &
# MANUAL CARS - BOTH MEANS ARE NOT EQUAL
# IF p-value IS LESS THAN 0.05, REJECT NULL & ACCEPT THE ALTERNATE
# IF p-value IS GREATER THAN 0.05, FAIL TO REJECT NULL & REJECT THE ALTERNATE
# 0.05 MEANS 95% CONFIDENCE LEVEL OR 5% ALPHA OR ERROR
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automatic=mtcars[mtcars.am==0]
manual=mtcars[mtcars.am==1]
# Subsetting or splitting the Datafarmes into 2 dataframes
# First one is only automatic cars and second only manual cars
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from scipy import stats as st
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# Scipy Stats package is for Statistical Modelling in Python
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st.ttest_ind(automatic.mpg,manual.mpg,equal_var=False)
# Since p-value is less than 0.05, REJECT NULL
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# Is the Average weight of Automatic & manual car is equal
# groupby wt with am - mean
# groupby wt with am - variance
# frame null & alternate hypothesis
# conduct the relevant hypotheis test
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mtcars.wt.groupby(mtcars.am).mean()
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mtcars.wt.groupby(mtcars.am).var()
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# NULL - THERE IS NO SIGNIFICANT DIFFERENCE IN AVERAGE weight OF AUTOMATIC &
# MANUAL CARS - BOTH MEANS ARE EQUAL
# ALTERNATE - THERE IS SIGNIFICANT DIFFERENCE IN AVERAGE weight OF AUTOMATIC &
# MANUAL CARS - BOTH MEANS ARE NOT EQUAL
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st.ttest_ind(automatic.wt,manual.wt,equal_var=False)
# since p-value is less than 0.05 Reject Null Hypothesis
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# ONLY TWO GROUPS - 2 SAMPLE INDEPENDENT TTEST
# MORE THAN 2 GROUPS - ANOVA SINGLE FACTOR
# Is the average mpg of heavyweight,mediumwt & lowweight cars is equal
# NULL - THERE IS NO SIGNIFICANT DIFFERENCE IN AVERAGE MPG OF HEAVY,MEDIUM &
# lOW WEIGHT CARS - ALL MEANS ARE EQUAL
# ALTERNATE - THERE IS SIGNIFICANT DIFFERENCE IN AVERAGE MPG OF HEAVY,MEDIUM &
# lOW WEIGHT CARS - ALL MEANS ARE NOT EQUAL
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mtcars.mpg.groupby(mtcars.weighttype).mean()
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heavywt=mtcars[mtcars.weighttype=='heavywt']
medwt=mtcars[mtcars.weighttype=='mediumwt']
lowwt=mtcars[mtcars.weighttype=='lowwt']
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st.f_oneway(heavywt.mpg,medwt.mpg,lowwt.mpg)
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# Is the Average MPG of ALL TYPES of GEARS CARS is Equal
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mtcars.mpg.groupby(mtcars.gear).mean()
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g3=mtcars[mtcars.gear==3]
g4=mtcars[mtcars.gear==4]
g5=mtcars[mtcars.gear==5]
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st.f_oneway(g3.mpg,g4.mpg,g5.mpg)
# Since p-value less than 0.05 Reject Null
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# BOTH VARIABLES ARE NON NUMERIC OR CATEGORICAL - CHI SQUARE TEST OF INDEPENDENCE
# INPUT OF CHI SQUARE TEST IS CROSS TABULATION (pd.crosstab())
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pd.crosstab(mtcars.mileagetype,mtcars.weighttype)
Out[54]:
In [55]:
st.chi2_contingency(pd.crosstab(mtcars.mileagetype,mtcars.weighttype))
# NULL - There is no relationship or association between both variables
# Alternate - There is relationship or association between both vriables
# Second item in output is p-value
# Since p-value is less than 0.05 Reject Null
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# Is there relationship or association between am & cyl?
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pd.crosstab(mtcars.am,mtcars.cyl)
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st.chi2_contingency(pd.crosstab(mtcars.am,mtcars.cyl))
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mtcars.dtypes
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In [60]:
objectcols=mtcars.select_dtypes(include=['object'])
# Select only Object or Character Columns
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numbercols=mtcars.select_dtypes(include=['number'])
# Select only Numerical Columns both float and int
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from sklearn.preprocessing import LabelEncoder
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le=LabelEncoder() # label Encoder used for Dummy Variable Encoding
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dummyobjectcols=objectcols.apply(le.fit_transform)
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dummyobjectcols1=dummyobjectcols.drop('Unnamed: 0',axis=1)
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mtcarsdf=pd.concat([numbercols,dummyobjectcols1],axis=1) # side by side merging
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mtcarsdf.head()
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In [68]:
y=mtcarsdf.mpg
X=mtcarsdf.drop('mpg',axis=1)
# y is the Dependent Variable & X is Independent Variables in one matrix
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from sklearn.linear_model import LinearRegression
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LinReg=LinearRegression() # Assigning function to short name
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LinRegmodel=LinReg.fit(X,y) # Fitting the model
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LinRegmodel.score(X,y) # Checking the R Square
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LinRegmodel.intercept_ # check intercept
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print(list(zip(X,LinRegmodel.coef_))) # check coefficients
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# mpg=23.3943+0.1164*cyl+0.0153*disp-0.024*hp-0.1415*drat-4.7944*wt+0.623*qsec...
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mpgpredicted=LinRegmodel.predict(X) # predicted mpg for all observations
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np.sqrt(np.mean((y-mpgpredicted)**2)) # Root Mean Square Error
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resid=y-mpgpredicted # Residuals
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np.sqrt(np.mean(resid**2)) # Root Mean Square Error Formula
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from sklearn.tree import DecisionTreeRegressor
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DecTree=DecisionTreeRegressor()
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DecTreemodel=DecTree.fit(X,y)
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DecTreemodel.score(X,y)
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DecTreepredict=DecTreemodel.predict(X)
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np.sqrt(np.mean((y-DecTreepredict)**2))
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from sklearn.ensemble import RandomForestRegressor
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RF=RandomForestRegressor(n_estimators=1000) # n_estimators=num of trees
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RFmodel=RF.fit(X,y)
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RFmodel.score(X,y)
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RFpredict=RFmodel.predict(X)
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np.sqrt(np.mean((y-RFpredict)**2))
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