Hinstogram using distplot seabron machine learing

 

#!/usr/bin/env python

# coding: utf-8

# # Hinstogram using distplot  seabron

# 

# In[96]:

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

from scipy.stats import norm

# # using gihub 

# In[13]:

# tips_df=sns.load_dataset("tips")

# tips.head()

# In[14]:

data=pd.read_csv("Startups.csv")

data.head()

# In[15]:

# sns.distplot(

#     a,#   wich are give you to  groph data  

#     bins=None,

#     hist=True,

#     kde=True,

#     rug=False,

#     fit=None,

#     hist_kws=None,

#     kde_kws=None,

#     rug_kws=None,

#     fit_kws=None,

#     color=None,

#     vertical=False,

#     norm_hist=False,

#     axlabel=None,

#     label=None,

#     ax=None,

# )

# In[16]:

sns.distplot(data["Administration"])

# In[17]:

sns.distplot(data["Profit"],bins=10)

# In[20]:

sns.distplot(data["Profit"],hist=False)

# In[22]:

sns.distplot(data["Profit"],kde=False)

# In[24]:

sns.distplot(data["Profit"],rug=True)

# In[18]:

sns.distplot(data["Profit"],fit=norm,)

# In[26]:

sns.distplot(data["Profit"],fit=norm,kde=False)

# In[29]:

sns.distplot(data["Profit"],axlabel="Profit ")

# In[31]:

sns.distplot(data["Profit"],axlabel="Profit",color="red",)

# In[37]:

sns.distplot(data["Profit"],axlabel="Profit",color="red")

# In[39]:

sns.distplot(data["Profit"],vertical=True,)

# In[41]:

sns.set()

sns.distplot(data["Profit"])

# In[45]:

plt.figure(figsize=(10,10))

sns.set()

sns.distplot(data["Profit"],)

# In[55]:

# bins1=[1,5,10,15,20,25,30,35,40,45,50]

# # plt.figure(figsize=(10,10))

# sns.set()

# sns.distplot(data["Profit"],bins = bins1)

# plt.xticks(bins)

# In[ ]:

# hist_kws=None,

#     kde_kws=None,

#     rug_kws=None,

#     fit_kws=None,

# In[70]:

sns.distplot(data["Profit"],hist_kws={'color':'red','edgecolor':"#aaff00",

                                      "linewidth":5,"linestyle":'--','alpha':0.8}

             kde_kws=None

            rug_kws=None

            fit_kws=None)

# In[74]:

sns.distplot(data["Profit"],hist_kws={'color':'red','edgecolor':"#aaff00",

                                      "linewidth":5,"linestyle":'--','alpha':0.8},

             kde_kws={'color':'blue',

                                      "linewidth":5,"linestyle":'--','alpha':0.8},

            rug_kws=None,

            fit_kws=None)

# In[81]:

sns.distplot(data["Profit"],hist_kws={'color':'red','edgecolor':"#aaff00",

                                      "linewidth":5,"linestyle":'--','alpha':0.8},

             kde_kws = None,

             rug_kws={'color':'blue','edgecolor':"#aaff00",

                                      "linewidth":13,"linestyle":'--','alpha':0.15},

            fit_kws=None)

# In[95]:

sns.distplot(data["Profit"],hist_kws={'color':'red','edgecolor':"#aaff00",

                                      "linewidth":5,"linestyle":'--','alpha':0.8},

             

             rug_kws={'color':'blue','edgecolor':"#aaff00",

                                      "linewidth":13,"linestyle":'--','alpha':0.15},

            fit_kws={'color':'bule','edgecolor':"#aaff00",

                                      "linewidth":13,"linestyle":'--','alpha':0.9})

# In[93]:

sns.distplot(data["Profit"],bins=9,label="Profit",)

sns.distplot(data["R&D Spend"],bins=9,label="R&D Spend")

sns.distplot(data["Administration"],bins=9,label="Administration")# R&D Spend   Administration\

plt.legend()

# In[94]:

sns.distplot(data["Profit"])

# In[ ]: