94 seaborn exercise in telugu public

how to import seaborn

how to load data set titanic

how to import mtplot lib

In [6]:

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('whitegrid')
titanic = sns.load_dataset('titanic')

In [39]:

#titanic.head(7)

In [5]:

# watch  5 rows in data set

In [7]:

Out[7]:

<seaborn.axisgrid.JointGrid at 0xb1e0f98>

In [40]:

sns.jointplot(x='fare',y='age',data=titanic)

Out[40]:

<seaborn.axisgrid.JointGrid at 0xe8f46d8>

In [9]:

Out[9]:

<matplotlib.axes._subplots.AxesSubplot at 0xbf65518>

In [41]:

sns.distplot(titanic['fare'])

Out[41]:

<matplotlib.axes._subplots.AxesSubplot at 0xea861d0>

In [42]:

sns.distplot(titanic['fare'],kde=False)

Out[42]:

<matplotlib.axes._subplots.AxesSubplot at 0xeaf1550>

In [43]:

sns.distplot(titanic['fare'],kde=False,color="r")

Out[43]:

<matplotlib.axes._subplots.AxesSubplot at 0xeb6d2b0>

In [47]:

sns.distplot(titanic['fare'],kde=False,color="r",bins=130)

Out[47]:

<matplotlib.axes._subplots.AxesSubplot at 0xedc2518>

In [16]:

Out[16]:

<matplotlib.axes._subplots.AxesSubplot at 0xc3229b0>

In [49]:

sns.boxplot(x='class',y='age',data=titanic)

Out[49]:

<matplotlib.axes._subplots.AxesSubplot at 0xf0df128>

In [50]:

sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')

Out[50]:

<matplotlib.axes._subplots.AxesSubplot at 0x1012a588>

In [22]:

Out[22]:

<matplotlib.axes._subplots.AxesSubplot at 0xc52d908>

In [51]:

sns.swarmplot(x='class',y='age',data=titanic)

Out[51]:

<matplotlib.axes._subplots.AxesSubplot at 0x10167c88>

In [52]:

sns.swarmplot(x='class',y='age',data=titanic,palette='Set2')

Out[52]:

<matplotlib.axes._subplots.AxesSubplot at 0x101b69b0>

In [27]:

Out[27]:

<matplotlib.axes._subplots.AxesSubplot at 0xc4137f0>

In [53]:

sns.countplot(x='sex',data=titanic)

Out[53]:

<matplotlib.axes._subplots.AxesSubplot at 0x10234828>

In [29]:

Out[29]:

Text(0.5,1,'titanic.corr()')

In [55]:

titanic.corr()
#sns.heatmap(titanic.corr(),cmap='coolwarm')

Out[55]:

	survived	pclass	age	sibsp	parch	fare	adult_male	alone
survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307	-0.557080	-0.203367
pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500	0.094035	0.135207
age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067	0.280328	0.198270
sibsp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651	-0.253586	-0.584471
parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225	-0.349943	-0.583398
fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000	-0.182024	-0.271832
adult_male	-0.557080	0.094035	0.280328	-0.253586	-0.349943	-0.182024	1.000000	0.404744
alone	-0.203367	0.135207	0.198270	-0.584471	-0.583398	-0.271832	0.404744	1.000000

In [56]:

sns.heatmap(titanic.corr(),cmap='coolwarm')
#plt.title('titanic.corr()')

Out[56]:

<matplotlib.axes._subplots.AxesSubplot at 0x10515ef0>

In [33]:

Out[33]:

<seaborn.axisgrid.FacetGrid at 0xde032e8>

In [57]:

g = sns.FacetGrid(data=titanic,col='sex')
#g.map(plt.hist,'age')

In [58]:

g = sns.FacetGrid(data=titanic,col='sex')
g.map(plt.hist,'age')

Out[58]:

<seaborn.axisgrid.FacetGrid at 0x1060dcc0>