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

#  # Installing Necessary Libraries

# In[1]:


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


from plotly.offline import iplot
import plotly as py
import plotly.tools as tls
import chart_studio.plotly as cpy
import chart_studio.tools as ctls


# In[3]:


import cufflinks as cf


# ## Using `cufflinks`  and `iplot()` to create different plots

# - line
# - scatter
# - bar
# - box
# - spread
# - histogram
# - bubble
# - surface
# - heatmap 

# ### Knowing the version of the packages

# In[1]:


print(py.__version__)


# In[10]:


print(cf.__version__)


# ### Embedding Online Plots to the Jupyter Notebook
# 

# In[4]:


ctls.embed('https://plotly.com/~cufflinks/8')


# ### Using Plotly-Cufflinks offline

# In[4]:


py.offline.init_notebook_mode(connected=True)


# In[5]:


cf.go_offline()


# ### Connecting Jupyter Notebook with Plotly Account

# In[11]:


import chart_studio
chart_studio.tools.set_credentials_file(username='PriyankaJha', api_key='EgKBYXoVIi8Aw32QlKrf')


# ## `lineplot`

# In[6]:


#generating some pseudo data for analysis
df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'])
df['A'] = df['A'].cumsum()
df['B'] = df['B'].cumsum()
df['C'] = df['C'].cumsum()
df.head()


# In[7]:


#different themes for plots in plotly
cf.getThemes()


# In[12]:


df.iplot(theme='solar')


# ## `scatterplot`

# In[13]:


df.iplot(x='A', y='B', mode='markers', symbol='diamond', size=9, theme='solar')
#use box or lasso select to highlight the desired markers


# ## `barplot`

# In[10]:


titanic = sns.load_dataset('titanic')
titanic.head()


# In[12]:


titanic.iplot(kind='bar',
              x='sex',
              y='survived',
              theme='solar',
              color='lightgreen',
              xTitle='Sex',
              yTitle='Number of Survived Individuals',
              title='Survival on the Basis of Sex')


# ## `stackedplot`

# In[14]:


df.iplot(kind='bar', bargap=0.4, barmode='stack', theme='solar')


# In[14]:


#horizontal bar graph
df.iplot(kind='barh', bargap=0.4, theme='solar')


# ## `boxplot`

# In[15]:


df.iplot(kind='box', theme='solar')


# ## `areaplot`

# In[16]:


#generating another set of dataset with only positive values
df2 = pd.DataFrame(np.random.randn(150, 3), columns=['A', 'B', 'C'])
df2['A'] = df2['A'].cumsum() + 15
df2['B'] = df2['B'].cumsum() + 15
df2['C'] = df2['C'].cumsum() + 15
df2.head()


# In[18]:


df2.iplot(kind='area', fill=True, theme='solar')


# ## `surfaceplot`

# In[19]:


df3 = pd.DataFrame({
    'X': [10, 20, 30, 20, 10],
    'Y': [10, 20, 30, 20, 10],
    'Z': [10, 20, 30, 20, 10]
})
df3


# In[19]:


#color scales for surface and heatmaps
cf.colors.scales()


# In[20]:


df3.iplot(kind='surface', colorscale='spectral')


# In[140]:


# help(cf.datagen)


# In[21]:


cf.datagen.sinwave(12, 0.15).iplot(kind='surface', colorscale='set1')


# ## `3Dsacatterplot`

# In[23]:


cf.datagen.scatter3d(2, 150, mode='stocks').iplot(kind='scatter3d',
                                                  x='x',
                                                  y='y',
                                                  z='z')


# ## `spreadplot`

# In[24]:


df[['A', 'B']].iplot(kind='spread', theme='solar')


# ## `histogram`

# In[25]:


df.iplot(kind='hist', bins=25, barmode='stack', theme='solar')


# ## `bubbleplot`

# In[26]:


cf.datagen.bubble3d(5, 6, mode='stocks').iplot(kind='bubble3d',
                                               x='x',
                                               y='y',
                                               z='z',
                                               size='size')


# ## `heatmap`

# In[27]:


cf.datagen.heatmap(17, 8).iplot(kind='heatmap',
                                colorscale='brbg',
                                title='Heatmap',
                                theme='solar')