# -*- coding: utf-8 -*-

"""

Created on Thu Jul 26 09:45:06 2018

DAY4, BITM, BALLARI

@author: pooja

"""

import numpy as np

np.abs(-56)

a=np.arange(6)

print(type(a))

b=np.arange(10,16)

#c=np.arange(100,201,4)

c=np.arange(20,26)

d=np.arange(30,36)

e=np.arange(40,46)

f=np.arange(50,56)

arr=np.array([a,b,d,c,e,f],dtype='str')

arr[0:2,2:]

"""

[[0, 2, 4],

[30, 32, 34],

[40, 42, 44]]

"""

arr[::2,::2]

arr1=np.arange(0,80,10)

l=[1,2,3,5,6,7]

a=np.array(l)

l1=[[1,2,3],[11,12,13],[21,22,23]]

a1=np.array(l1)

a2=np.arange(0,11,2)

a3=np.zeros(3)

a4=np.ones((3,2))

a5=np.linspace(0,20,50)

a6=a5.reshape(10,5)

a6.shape

aa=np.full((3,4),12)

from numpy import random

random.randint(4,56)

arand=random.rand(3,5)

arn=random.randn(4,4)

a6=np.eye(4)

a7=np.random.rand(5) #uniform dist a8=np.random.randn(2)

a_7=np.random.rand(5,4)

arr1=np.random.randint(5,100,4)

arr1.max()

arr1.min()

a1.max()

arr1.sum()

a1.sum()

a1.sum(axis=0)

"""

array([33, 36, 39])

"""

#index location of max n min

arr1.argmin()

arr1.argmax()

arand.argmax()

arr1[2]

#element at index 2?

a=np.arange(0,50)

a_slice=a[:6]

a_slice[:]=80

#so numpy doesnot

#created a new array for

#a_slice.

#for copy

slice_a_copy=a[:6].copy()

slice_a_copy[:]=67

a1.std()

a1.var()

arr1.mean()

arr1.median()

a1=[1,2,3,4]

a2=[10,11,12,13]

#a2-a1

a1=np.array(a1)

a2=np.array(a2)

a2-a1

a1-a2

a1*a2

a1=a1*2

a1/a1

asq=np.sqrt(a1)

np.exp(a1)

a1=np.inf

a1=np.nan

np.isnan(a1)

a2[0]=90

a2[3]=89

a2.sort()

np.insert(a2,1,67)

a6.cumsum(axis=0)

np.delete(a2,[3])

np.append(a2,[90,67,78])

a2[1].size()

aaa=np.add(a2,a6)

np.subtract(a2,a6)

np.multipy()

np.split(a,6)

a2>20

af=a6.ravel()

np.concatenate((aa,aaa), axis=1)

#--------------------------------------------------------------------------

#PANDAS

#---------------------------------------------

import pandas as pd

import numpy as np

labels=['a','b','c','d']

data=[10,20,30,40]

arr=np.array(data)

d={'a':10,'b':20,'c':30,'d':40}

s=pd.Series(data)

s1=pd.Series(data, labels)

s2=pd.Series([10,20,30,40] , ['a','b','c','d'])

sd=pd.Series(d)

type(sd)

sd.dtype

s1['d']

l1=[[1,2,3],[22,1,12],[21,22,23]]

a1=np.array(l1)

ss=pd.DataFrame(a1)

type(ss)

ss1=pd.DataFrame(a1,['a','b','c'], columns=['x','y','z'])

ss1['x']['b']

ss1['y']['c']

sx=ss1.x

sx=ss1['x']

s=pd.Series([10,34,6,34], index=['a','c','d','e'])

s3 = pd.Series([7, 4,-2, 3],index=['a','b', 'c', 'd'])

s + s3

s.add(s3, fill_value=0)

s.sub(s3, fill_value=2)

s.div(s3, fill_value=4)

s[~(s > 16)]

#Series s where value is not >1 >>>

s[(s < -1) | (s > 2)] #s where value is <-1 or >2 >>>

df=pd.DataFrame(a1)

type(df)

df=pd.DataFrame(a1,['a','b','c'], columns=['x','y','z'])

df.iloc[[0],[2]]

df.iat[0,2]

df.at['a','y']

df.loc['a','y']

df.ix[2]

df.ix[2,'z']

df.shape

df.index

df.columns

df.info()

df.sort_index()

df.sort_values(by='y')

df.rank()

df.drop(['a', 'c']) #Drop values from rows (axis=0)

df.drop('Country', axis=1)

f = lambda x: x*2

df.apply(f)

df.applymap(f)

df.sum(axis=1)

df.cumsum()

df.min()

df.max()

df.idxmin()

df.idxmax()

df.describe()

df.mean()

df.median()

data=pd.read_csv('TST.csv')

data.info()

data.describe()

data.index

data.columns

age=data['Age']

data.Age

aasix=age[age>60]

len(aasix)

#-------------------------------------------------------------

import pandas as pd

D1=pd.read_csv('TST.csv')

D1

D1.info()

D1.describe()## to see the max,min,mean,std,count and quartiles

## to axcess a particular column

D1['Age']

D1.Age[0:5]

D1.head(20)

D1.tail()

nudata=D1[['Age', 'Survived']]

## to access particular column n row

D1[['Survived','Age']][10:50]

## to acess particular columns n rows

D1['Pclass'].unique()

D1['Age'].value_counts()

##gives the counts in a particular column

D1['Age'].value_counts(normalize=True)*10000

pd.crosstab(D1.Sex,D1.Survived)

### combining th condition and combin 2 tables with a particualr condition

pd.crosstab(D1.Sex,D1.Survived,normalize="index")

## normalize by index

pd.crosstab(D1.Sex,D1.Survived,normalize="columns")

## normalise by column

pd.crosstab(D1.Sex,D1.Survived,normalize="all")

##normalise everything

age5=D1[D1['Age']<=5]

age5.shape

D1[D1.Age<=5][['Survived','Pclass']]

age5[0:3]

len(age5<5)

## to count the sum f the particular column with a condition

D1[D1.Age<=5]['Survived'].value_counts()

D1[D1.Name.str.contains("Allen")]

D1.Embarked.unique()## to calculate the unique values

D1['Age'].value_counts(dropna=False)## to see the nas as unique values

D1['Embarked'].value_counts(dropna=False)

D1[(D1.Age<=5)& (D1.Survived==1)][['Name','PassengerId']]

D1[(D1.Age<=5)& (D1.Survived==1)][['Name','PassengerId']][0:10]

D1.groupby('')['Age'].mean()

D1.groupby('Survived')['Age'].mean()## to do some function in a catogorical column

D1.groupby(['Pclass','Survived'])['Age'].mean()

D1.groupby(['Pclass','Survived'])['Age'].sum()

D1.groupby(['Pclass','Survived'])['Age'].mean().reset_index()## to do the mean and do rename the index

#-------------------------------------

pd.read_excel('file.xlsx')

pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')

#Read multiple sheets from the same file

xlsx = pd.ExcelFile('file.xls')

df = pd.read_excel(xlsx, 'Sheet1')

pd.read_csv('file.csv' ,header=None, nrows=5)

df.to_csv('myDataFrame.csv'

df[df[['Population']>1200000000]]