# Import libraries and DataFrame
#
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
# Read the data from pokeman.csv into a DataFrame using pandas read_csv()
# Print out the first 6 lines of data using .head
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
df=pd.read_csv('pokeman.csv')
df.head(6)
#int64
Nameobject
0
1
Bulbasaur
1
2
Ivysaur
2
3
Venusaur
3
4
Charmander
4
5
Charmeleon
5
6
Charizard
# print out the data types of all features using .dtypes (no parentheses)
df.dtypes
# print out the column names using .columns
df.columns
# Create a pandas Series for the feature Speed; print out type
df.Speed
s_series=df.Speed
type(s_series)
# Create a NumPy array for the feature Speed (use.values) ; print out type
s=df.Speed.values
type(s)
# Make 1D NumPy arrays from the features Attack and Defense and do a scatter plot
# using matplotlib
a=df.Attack.values
d=df.Defense.values
plt.plot(a,d,'ro')
# Create a new DataFrame "df_mod" which is same as original but we drop "Type 2" feature; print out to check
df_mod=df.drop(columns=["Type 2"])
df_mod
#int64
1 - 151
Nameobject
Bulbasaur0.7%
Ivysaur0.7%
149 others98.7%
0
1
Bulbasaur
1
2
Ivysaur
2
3
Venusaur
3
4
Charmander
4
5
Charmeleon
5
6
Charizard
6
7
Squirtle
7
8
Wartortle
8
9
Blastoise
9
10
Caterpie
# Import libraries and DataFrame
#
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
# Read the data into a DataFrame
# Print out the first 5 lines of data
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
df=pd.read_csv('pokeman.csv')
df.head()
#int64
Nameobject
0
1
Bulbasaur
1
2
Ivysaur
2
3
Venusaur
3
4
Charmander
4
5
Charmeleon
# Add a white grid to the background of Seaborn plots using set_style
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
df=pd.read_csv('pokeman.csv')
df.head()
sns.set_style("whitegrid")
# Make a scatter plot using Seaborn's relplot of Defense statistics (y-axis)
# vs Attacks Stats
sns.relplot(x='Attack',y='Defense',data=df)
# Repeat plot in previous cell but use color to indicate Type 1 (hue = )
sns.relplot(x='Attack',y='Defense',data=df,hue='Type 1')
# Make a category plot of Defense statistics vs Type 1 (non-numerical)
# Rotation labels on x-axis for readability using plt.xticks using plt.xticks(rotation=-45)
sns.catplot(x='Type 1',y='Defense',data=df)
plt.xticks(rotation=-45)
# Make a Bar graph of Defense statistics for Type 1
sns.barplot(x='Type 1',y='Defense',data=df)
plt.xticks(rotation=-45)
# Make a violin plot of the Defense data for Type 1
sns.violinplot(x='Type 1',y='Defense',data=df)
plt.xticks(rotation=-45)
# Repeat the plot in the previous cell but change palette to 'prism' and change size
sns.violinplot(x='Type 1',y='Defense',data=df,palette='prism',size=6)
plt.xticks(rotation=-45)
# Overlaying plots - overlay violin plot of Defense with actual points
# To do this (1) increase figure size using ```plt.figure(figsize = (10,6) )```;
# (2) create violin plot and set inner = None to get rid of the bars inside violin plot;
# (3) rotate x-axis labels for readability;
# (4) create swarmplot for points and set ```color='k'``` to create the points in black;
# (5) add title "Defense Data for Type 1"
#
plt.figure(figsize = (10,6))
sns.violinplot(x='Type 1',y='Defense',data=df,palette='prism',inner=None)
sns.swarmplot(x='Type 1',y='Defense',data=df,color='k')
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
# Import LinearRegression function from scikit-learn
from sklearn.linear_model import LinearRegression
# Read in data from file insurance.csv and create a DataFrame; print out some lines
#
df=pd.read_csv('insurance.csv')
df.head()
ageint64
sexobject
0
19
female
1
18
male
2
28
male
3
33
male
4
32
male
# Set background grid for Seaborn plots
sns.set_style("whitegrid")
# Create scatter plot of charges vs BMI with color indiciating whether patient is
# smoker or not
sns.relplot(y='charges',x='bmi',data=df,hue='smoker')
# Get data to use for linear regression
# Right now we see if there is a relationship between insurance charges and bmi
charges=df.charges.values
bmi=df.bmi.values
# Make bmi an n by 1 array and charges n by 1
n=len(bmi)
charges=np.reshape(charges,(n,1))
bmi=np.reshape(bmi, (n,1))
# Create model and fit data
lr=LinearRegression()
lr.fit(bmi,charges)
# write out equation of line
print("Intercept is",lr.intercept_[0])
print("Slope is",lr.coef_[0,0])
Intercept is 1192.9372089611497
Slope is 393.87303079739524
# Use regplot to plot data and line
sns.regplot(x='bmi',y='charges',data=df)
# predict insurance costs for a person with BMI 31.7; round answer to nearest cent
x_eval=np.array([31.7])
x_eval=np.reshape(x_eval,(1,1))
lr.predict(x_eval)
#
# Note that this value agrees with plot above because when x=31.7 y is around 14,000