%pylab inline

from pandas import Series, DataFrame

import pandas as pd

obj = Series([4, 7, -5, 3])

obj

obj.values

obj.index

obj2 = Series([4,7,-5,3], index = ['d','b','a','c'])

obj2

obj2.values

obj2.index

obj2['a']

obj2[['c','a','d']]

obj2[obj2>0]

obj2*2

np.exp(obj2)

'b' in obj2

'e' in obj2

sdata = {'Ohio':3500,'Texas':71000,'Oregon':16000, 'Utah':5000}

obj3 = Series(sdata)

obj3

states = ['California','Ohio','Oregon','Texas']

obj4 = Series(sdata, index=states)

obj4

pd.isnull(obj4)

pd.notnull(obj4)

obj4.isnull()

obj3

obj4

obj3 + obj4

obj4.name = 'population'

obj4.index.name='state'

obj4

obj.index = ['Bob','Steve','Jeff','Ryan']

obj

data = {'state':['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
        'year':[2000, 2001, 2002, 2001, 2002],
        'pop':[1.5, 1.7, 3.6, 2.4, 2.9]}
frame = DataFrame(data)

frame

frame2 = DataFrame(data, columns = ['year','state','pop','debt'],
                   index=['one','two','three','four','five'])

frame2

frame2.columns

frame2['state']

frame2.year

frame2.ix['three']

frame2.ix[2]

frame2['debt']=16.5

frame2

frame2['debt']=np.arange(5.)

frame2

val = Series([1.2, 1.5, -1.7], index = ['two', 'four','five'])

frame2['debt']=val

frame2

frame2['eastern'] = frame2.state=='Ohio'

frame2

del frame2['eastern']

frame2.columns

pop={'Nevada':{2001:2.4, 2002:2.9},
     'Ohio': {2000:1.5, 2001:1.7,2002:3.6} }

frame3 = DataFrame(pop)

frame3

frame3.T

DataFrame(pop, index=[2001, 2002, 2003])

pdata = {'Ohio':frame3['Ohio'][:-1],
         'Nevada':frame3['Nevada'][:2]}

DataFrame(pdata)

frame3.index.name='year'

frame3.columns.name = 'state'

frame3

frame3.values

frame2.values

obj = Series(range(3), index=['a','b','c'])

index = obj.index

index[1:]

index[1]='d'

index = pd.Index(np.arange(3))

obj2 = Series([1.5,-2.5,0], index=index)

obj2.index is index

frame3

'Ohio' in frame3

2003 in frame3.index

frame3.index-frame2.index

frame3.index.diff(frame2.index)

obj = Series([4.5,7.2,-5.3,3.6], index=['d','b','a','c'])

obj

obj2 = obj.reindex(['a','b','c','d','e'])

obj2

obj.reindex(['a','b','c','d','e'], fill_value=0)

obj

obj3 = Series(['blue','purple','yellow'], index=[0,2,4])

obj3.reindex(range(6))

obj3.reindex(range(6),method='ffill') # forward fills the values

frame = DataFrame(np.arange(9).reshape(3,3), index = ['a','c','d'],
                  columns=['Ohio','Texas','California'])

frame

frame2 = frame.reindex(['a','b','c','d'])

frame2

states = ['Texas','Utah','California']

frame.reindex(columns=states)

frame.reindex(index=['a','b','c','d'], method='ffill',
              columns=states)

frame

frame.ix[['a','b','c','d'], states]

obj = Series(np.arange(5.), index = ['a','b','c','d','e'])

obj

new_obj = obj.drop('c')

new_obj

obj.drop(['d','c'])

data = DataFrame(np.arange(16).reshape((4,4)),
                 index = ['Ohio','Colorado','Utah','New York'],
                 columns = ['one','two','three','four'])

data

data.drop(['Colorado','Ohio'])

data.drop('two', axis=1)

data.drop(['two','four'], axis=1)

obj = Series(np.arange(4.), index=['a','b','c','d'])

obj['b']

obj[2:4]

obj[['b','a','d']]

obj[[1,3]]

obj[obj<2]

obj['b':'c']

obj['b':'c']=5

obj

data=DataFrame(np.arange(16).reshape((4,4)),
               index=['Ohio','Colorado','Utah','New York'],
               columns=['one','two','three','four'])

data

data['two']

data[['three','one']]

data[:2]

data[data['three']>5]

data<5

data[data<5]=0

data

data.ix['Colorado',['two','three']]

data.ix[['Colorado','Utah'],[3,0,1]]

data.ix[2]

data.ix[:'Utah','two']

data.ix[data.three>5,:3]

s1 = Series([7.3,-2.5,3.4,1.5], index=['a','c','d','e'])

s2 = Series([-2.1,3.6,-1.5,4,3.1], index=['a','c','e','f','g'])

s1

s2

s1+s2

df1 = DataFrame(np.arange(9.).reshape((3,3)),
                columns = list('bcd'),
                index=['Ohio','Texas','Colorado'])

df2 = DataFrame(np.arange(12.).reshape((4,3)),
               columns = list('bde'),
               index=['Utah','Ohio','Texas','Oregon'])

df1

df2

df1 + df2

df1 = DataFrame(np.arange(12.).reshape((3,4)), columns = list('abcd'))

df2 = DataFrame(np.arange(20.).reshape((4,5)), columns = list('abcde'))

df1

df2

df1 + df2

df1.add(df2, fill_value=0)

df1.reindex(columns=df2.columns, fill_value=0)

arr = np.arange(12.).reshape((3,4))

arr

arr[0]

arr-arr[0]

frame = DataFrame(np.arange(12.).reshape((4,3)), columns=list('bde'),
                  index=['Utah','Ohio','Texas','Oregon'])

frame

series =frame.ix[0]

series

frame-series

series2 = Series(range(3), index=['b','e','f'])

series2

frame + series2

series3 = frame['d']

frame

series3

frame.sub(series3, axis=0)

import numpy as np

frame = DataFrame(np.random.randn(4,3), columns=list('bde'),
                  index=['Utah','Ohio','Texas','Oregon'])

frame

np.abs(frame)

f = lambda x: x.max()-x.min()

frame.apply(f,axis=1)

def f(x):
    return Series([x.min(),x.max()], index=['min','max'])

frame.apply(f, axis=0)

format = lambda x: '%.2f' %x

frame.applymap(format)

frame['e'].map(format)

obj = Series(range(4), index=['d','a','b','c'])

obj.sort_index()

frame = DataFrame(np.arange(8).reshape((2,4)), index = ['three','one'],
                  columns = ['d','a','b','c'])

frame.sort_index()

frame.sort_index(axis=1)

frame.sort_index(axis=1, ascending = False)

obj = Series([4,7,-3,2])

obj.order()

obj = Series([4,np.nan,7,np.nan,-3,2])

obj.order()

frame = DataFrame({'b':[4,7,-3,2],'a':[0,1,0,1]})

frame

frame.sort_index(by='b')

frame.sort_index(by = ['a','b'])

obj = Series([7, -5, 7, 4, 2, 0, 4])

obj.rank()

obj.rank(method='first')

obj.rank(ascending=False, method='max')

frame = DataFrame({'b':[4.3,7,-3,2], 'a':[0,1,0,1],
                   'c':[-2,5,8,-2.5]})

frame

frame.rank(axis=1)

obj = Series(range(5),index=['a','a','b','b','c'])

obj

obj.index.is_unique

obj['a']

obj['c']

df = DataFrame(np.random.randn(4,3), index=['a','a','b','b'])

df

df.ix['b']

df = DataFrame([[1.4, np.nan], [7.1,-4.5],
                [np.nan, np.nan],[0.75,-1.3]],
               index=['a','b','c','d'],
               columns=['one','two'])

df

df.sum()

df.sum(axis=1) 

df.mean(axis=1, skipna=False)

df.idxmax()

df.cumsum()

df.describe()

obj = Series(['a','a','b','c']*4)

obj.describe()

import pandas.io.data as web

all_data = {}

for ticker in ['AAPL','IBM','MSFT','GOOG']:
    all_data[ticker] = web.get_data_yahoo(ticker,'04/11/2010','04/14/2014')

price =  DataFrame({tic: data['Adj Close']
                    for tic, data in all_data.iteritems()})

volume = DataFrame({tic: data['Volume']
                    for tic, data in all_data.iteritems()})

returns = price.pct_change()

returns.tail()

returns.MSFT.corr(returns.IBM)

returns.MSFT.cov(returns.IBM)

returns.corr()

returns.cov()

returns.corrwith(returns.IBM)

returns.corrwith(volume)

obj = Series(['c','a','d','a','a','b','b','c','c'])

uniques = obj.unique()

uniques

obj.value_counts()

pd.value_counts(obj.values, sort=False)

mask = obj.isin(['b','c'])

mask

obj[mask]

data = DataFrame({'Quo1':[1,3,4,3,4],
                  'Quo2': [2,3,1,2,3],
                  'Quo3':[1,5,2,4,4]})

data

result = data.apply(pd.value_counts).fillna(0)

result

string_data = Series(['aardvark','artichoke',np.nan, 'avocado'])

string_data

string_data.isnull()

string_data[0] = None

string_data.isnull()

from numpy import nan as NA

data = Series([1, NA, 3.5, NA, 7])

data.dropna()

data[data.notnull()]

data = DataFrame([[1.,6.5,3.],[1.,NA,NA],
                  [NA,NA,NA],[NA,6.5,3.]])

data

cleaned = data.dropna()

cleaned

data.dropna(how='all')

data[4]=NA

data

data.dropna(axis=1,how='all')

df = DataFrame(np.random.randn(7,3))

df.ix[:4,1]=NA; df.ix[:2,2]=NA

df

df.dropna(thresh=2)

df.fillna(0)

df.fillna({1:0.5,3:-1})

_ = df.fillna(0, inplace=True)

df

df = DataFrame(np.random.randn(6,3))

df.ix[2:,1]=NA; df.ix[4:,2]=NA

df

df.fillna(method = 'ffill')

df.fillna(method='ffill',limit=2)

data=Series([1.,NA,3.5,NA,7])

data.fillna(data.mean())

data = Series(np.random.randn(10),
              index = [['a','a','a','b','b','b','c','c','d','d'],
                       [1,2,3,1,2,3,1,2,2,3]])

data

data.index

data['b']

data['b':'c']

data.ix[['b','d']]

data[:,2]

data.unstack()

data.unstack().stack()

frame = DataFrame(np.arange(12).reshape((4,3)),
                  index = [['a','a','b','b'],[1,2,1,2]],
                  columns = [['Ohio','Ohio','Colorado'],
                             ['Green','Red','Green']])

frame

frame.index.names = ['key1', 'key2']

frame.columns.names = ['state', 'color']

frame

frame['Ohio']

frame.swaplevel('key1','key2')

frame.sortlevel(1)

frame.sortlevel(0)

frame.swaplevel(0,1).sortlevel(0)

frame

frame.sum(level='key2')

frame.sum(level='color',axis=1)

frame = DataFrame({'a':range(7), 'b':range(7,0,-1),
          'c':['one','one','one','two','two','two','two'],
          'd':[0,1,2,0,1,2,3]})

frame

frame2 = frame.set_index(['c','d'])

frame2

frame.set_index(['c','d'], drop=False)

frame2.reset_index()

ser = Series(np.arange(3.))

ser[-1]

ser

ser2 = Series(np.arange(3.), index = ['a','b','c'])

ser2[-1]

ser.ix[:1]

ser3 = Series(range(3), index = [-5, 1,3])

ser3.iget_value(2)

frame = DataFrame(np.arange(6).reshape(3,2), index=[2,0,1])

frame

frame.irow(0)

frame.irow(2)