#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np


# In[3]:


A = np.array([[1, 2, 3], 
              [2, 3, 4]])
u = np.array([10, 20, 30])


# In[4]:


A + u


# In[5]:


A * u


# In[6]:


T = np.array([[[2, 3],
               [4, 1]],
              [[1, 10],
               [5, 9]]])


# In[7]:


T.shape


# In[8]:


T + np.array([100, 200])


# In[15]:


T.shape


# In[11]:


T + np.array([[100, 200]])


# In[13]:


np.array([[100, 200]]).shape


# In[12]:


T + np.array([[100], [200]])


# In[14]:


np.array([[100], [200]]).shape


# In[16]:


np.array([[1, 2, 3]]) + np.array([[10], [20]])


# In[17]:


np.array([[1, 2, 3]]) * np.array([[10], [20]])


# In[19]:


np.array([[10], [20]]) @ np.array([[1, 2, 3]])


# In[21]:


np.array([1, 2, 3, 4, 5, 6]).reshape(2, 3)


# In[22]:


np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 3)


# In[23]:


np.array([1, 2, 3, 4, 5, 6]).reshape(3, -1)


# In[24]:


np.array([1, 2, 3, 4, 5, 6]).T


# In[26]:


np.array([1, 2, 3, 4, 5, 6]).reshape(1, -1).T


# In[28]:


np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)


# In[29]:


np.array([1, 2, 3, 4, 5, 6]).reshape(2, 1, 3)


# In[30]:


x = np.array([1, 2, 3, 4, 5, 6])


# In[31]:


x


# In[33]:


x[x % 2 == 0] = 0


# In[34]:


x


# In[43]:


x = np.array([1, 2, 3, 4, 5, 6])
y = np.array([10, 20, 30, 40, 50, 60])
np.where(x % 2 == 0, 100, x)


# In[45]:


np.linalg.solve(np.array([[3, 2], 
                          [1, 4]]), 
                np.array([10, 20]))


# In[62]:


np.random.multivariate_normal(np.array([0, 0]),
                              np.array([[1, 0.7],
                                        [0.7, 1]]), size=5).T


# In[80]:


x, y = np.random.multivariate_normal(np.array([0, 0]),
                              np.array([[1, 0.9],
                                        [0.9, 1]]), size=100).T


# In[81]:


import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[82]:


plt.plot(x, y, 'o')


# In[83]:


import pandas as pd


# In[84]:


s = pd.Series([10, 20, 30])


# In[85]:


s


# In[86]:


s = pd.Series([10, 20, 30], index=['Alice', 'Bob', 'Claudia'])


# In[87]:


s


# In[88]:


s['Alice']


# In[89]:


s[0]


# In[90]:


s[1:]


# In[95]:


u = pd.Series([30, 10, 40], index=['Angela', 'Alice', 'Claudia'])


# In[96]:


s


# In[97]:


u


# In[98]:


u + s


# In[100]:


nan = float("NaN")


# In[102]:


x = nan
if x == nan:
    print("x is NaN")


# In[103]:


float("NaN") == float("NaN")


# In[105]:


x = nan
if x != x and isinstance(x, float):
    print("x is NaN")


# In[108]:


class MyNaN(object):
    def __eq__(self, other):
        return False


# In[109]:


x = MyNaN()
y = MyNaN()
x == y


# In[110]:


None


# In[111]:


None == float("NaN")


# In[112]:


None == None


# In[115]:


pd.isna(u + s).sum()


# In[116]:


s


# In[117]:


s.mean()


# In[118]:


s.sum()


# In[119]:


s


# In[120]:


s['Alice']


# In[121]:


s[0]


# In[122]:


numbers = pd.Series([10, 20, 30, 40], index=[3, 2, 0, 1])


# In[123]:


numbers


# In[125]:


numbers[0]


# In[126]:


numbers.iloc[0]


# In[128]:


numbers.loc[0]


# In[129]:


s


# In[130]:


s = pd.Series([10, 20, 30, 40, 50], index=['Alice', 'Bob', 'Claudia', 
                                   'Daniel', 'Mark'])


# In[131]:


s


# In[132]:


s[1:3]


# In[136]:


s['Bob':'Daniel']


# In[135]:


s[:2]


# In[137]:


s


# In[138]:


s['Anna':'Dmitry']


# In[143]:


s = pd.Series([10, 20, 30, 40, 50], index=['Bob', 'Claudia', 
                                   'Daniel','Alice', 'Mark'])


# In[144]:


s


# In[145]:


s['Anna':'Dmitry']


# In[146]:


s['Daniel':'Mark']


# In[147]:


uuu = pd.Series([10, 20, 30, 40, 50], index=['Bob', 'Bob', 
                                   'Bob','Bob', 'Mark'])


# In[149]:


uuu['Bob']


# In[150]:


uuu['Mark']


# In[151]:


uuu['Bob':'Mark']


# In[153]:


uuu + pd.Series([100, 200, 300, 400], 
                index=['Bob', 'Bob', 'Mark', 'Mark'])


# In[154]:


s


# In[155]:


s


# In[156]:


s[s > 20]


# In[157]:


s > 20


# In[158]:


b = pd.Series([0, 'hello'])


# In[162]:


b.replace({'hello': np.nan})


# In[165]:


pd.Series([float("NaN"), 1]).replace({float("NaN"): 100})


# In[169]:


pd.Series([float("NaN"), 1]).fillna(100)


# In[170]:


pd.Series([None, 1]).fillna(100)


# In[172]:


pd.Series([1, None, 2, 3, None, 4, None]).fillna(method='backfill')


# In[173]:


pd.Series([1, None, 2, 3, None, 4]).fillna(method='ffill')


# In[178]:


pd.Series([1, None, 2, 3, None, None, 4]).interpolate(
  method='quadratic')


# In[ ]: