from IPython.display import HTML
HTML('<iframe src="http://127.0.0.1:8888" width=600 height=400></iframe>')

import numpy as np

data = np.array([[0.1, 0.3], [1.4, -3.1]], dtype='float64')
data

data * 10

data + 3*(np.sin(data)-1)

np.ones((2,3))

data = np.arange(6)
data

data.reshape(2,3)

np.random.rand(6)

data = np.arange(10)
data

data[3]

data[6:9]

data[::2]

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

data[1:3,1:4]

data[[0,2,2],[1,2,3]]

data

data[data > 4]

data[data % 3 != 0]

data[data % 3 == 0] = 100
data

x = np.arange(1,7,dtype='float64').reshape(2,3)
x

y = np.arange(8,14,dtype='float64').reshape(3,2)
y

y.dot(x)

from numpy.linalg import inv,qr
X = np.random.randn(3,3)
mat = X.T.dot(X)
inv(mat)

mat.dot(inv(mat))

q, r = qr(mat)
r

import matplotlib.pyplot as plt

%matplotlib inline

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
x = np.linspace(0, 3*np.pi, 1000)
y = np.sin(x**2)
ax1.plot(x,y);

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
x = np.linspace(0, 3*np.pi, 1000)
y = np.sin(x**2)

ax1.plot(x,y, label='$\sin(x^2)$');
ax1.set_xticks([np.pi * i for i in range(0,4)])
ax1.set_xticklabels(['$0$', '$\pi$', '$2\pi$', '$3\pi$'], fontsize='x-large') 
ax1.set_title('My Plot', fontsize='x-large')
ax1.legend(loc='best', fontsize='x-large')
ax1.set_xlabel('$x$', fontsize='x-large');

fig = plt.figure()
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)

ax1.hist(np.random.randn(100), bins=20, color='g', alpha=0.3);
ax2.scatter(np.arange(30), np.arange(30) + 3*np.random.randn(30));
ax3.plot(np.random.randn(50).cumsum(), 'r--');

import pandas as pd

pd.Series(np.random.randn(3), index = ['First', 'Second', 'Third'] )

df=pd.DataFrame(np.random.randn(6).reshape(2,3), columns = ['Alice', 'Bob', 'Charles'], index=["One", "Two"])
df

df['Alice']

type(df['Alice'])

df.ix['One',:]

df.ix[0,[0,2]]

func = lambda x: x + 100
df.apply(func)

df.sum()

df1 = pd.DataFrame({'key': ['a', 'a', 'b', 'a', 'c'], 'data1': range(5)})
df1

df2 = pd.DataFrame({'key': ['a', 'b', 'c'], 'data2': range(3,6)})
df2

pd.merge(df2, df1)

df1 = pd.DataFrame(np.arange(12).reshape(3,4), index=['One', 'Two', 'Three'], columns=['A', 'B', 'C', 'D'])
df1

df2 = pd.DataFrame(np.arange(8,17).reshape(3,3), index=['Three', 'Four', 'Five'],columns=['A', 'B', 'C'])
df2

df3 = pd.concat([df1, df2])
df3

df3 = df3.drop_duplicates(subset=['A'],)
df3

from datetime import datetime
df3.index=[datetime(2014, 1, 3), datetime(2014, 1, 6), datetime(2014, 1, 14), datetime(2014, 2, 1), datetime(2014, 3, 25)]
df3.index.name = 'Dates'
df3.columns.name = 'Class'
df3

df3.index[0]

df3.plot();

df.head()

group_sums=df.groupby('Category').sum()
group_sums.head()

pie_data = -group_sums.ix[['Car', 'Cash', 'Food', 'Kids', 'Media','Restaurant', 'Sports'],'Value']
pie_data.plot(kind='pie', title='Sum of categorized expenses');

df['2/1/2014':'4/1/2014'].groupby('Category').sum().head()

food = df.groupby('Category').get_group('Food')
food.head(3)

food.plot(style = 'o-');

food_monthly_timestamp = food['Value'].resample('M', how='sum')
food_monthly_timestamp.head(4)

food_monthly = food_monthly_timestamp.to_period()
food_monthly.head(4)

food_monthly.plot(style = 'o-');

food_change = food_monthly / food_monthly.shift(-1) -1
food_change[:-3]

food_change.plot(style = 'o-');

me = pd.DataFrame()
for group in ['Car', 'Cash', 'Food', 'Kids', 'Media','Restaurant']:
    group_expenses = df.groupby('Category').get_group(group)
    group_expenses_monthly = -group_expenses['Value'].resample('M', how='sum', kind='period')
    me[group] = group_expenses_monthly
me

me.plot(kind='barh', stacked=True, alpha=0.8);