<jupyter_start><jupyter_text>Import Libraries<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns<jupyter_output><empty_output><jupyter_text>Read Training Data<jupyter_code>train = pd.read_csv("train.csv")

train.head()<jupyter_output><empty_output><jupyter_text>Training Data Statistics<jupyter_code># Data Dictionary with first column being datatype.
data = pd.DataFrame(train.dtypes)
data
train.shape
train.describe()
train.info()
train["Survived"].value_counts()
train.groupby("Sex")["Survived"].value_counts()

data["Missing value"]=train.isnull().sum()
data
# Identifying the count of the variables

data["Count"]=train.count()
data
data = data.rename(columns={0:'DataType'})
data
sns.countplot(x="Survived",data=train)
train.head()
train_to_plot = train.groupby("Survived").count()
train_to_plot

train.figure()

train_to_plot.count().plot()
 = train.set_index("Survived")
train_to_plot.head()
train_to_plot_group_Age = train.groupby(by='Survived').count()
train_to_plot_group_Age

train_to_plot_group_Age.plot().Survived<jupyter_output><empty_output>
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