# 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 df = pd.read_csv ('pokeman.csv') df.head (6)

# print out the data types of all features using .dtypes (no parentheses) df = pd.read_csv ('pokeman.csv') df.dtypes

# print out the column names using .columns df = pd.read_csv('pokeman.csv') df.columns

# Create a pandas Series for the feature Speed; print out type df.Speed

# Create a NumPy array for the feature Speed (use.values) ; print out type sp = df.Speed.values print (type(sp)) print (sp)

# Make 1D NumPy arrays from the features Attack and Defense and do a scatter plot # using matplotlib mf = df.Attack.values af = df.Defense.values plt.plot (mf, af, 'ro') plt.ylabel ("Attack") plt.xlabel ("Defense")

# Create a new DataFrame "df_mod" which is same as original but we drop "Type 2" feature; print out to check

# 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 df = pd.read_csv ('pokeman.csv') df.head (5)

# Add a white grid to the background of Seaborn plots using set_style 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) plt.title ("defense vs attack")

# Repeat plot in previous cell but use color to indicate Type 1 (hue = ) sns.relplot (x= 'Attack', y = 'Defense,', hue = 'Type 1') plt.title ("Defense vs Attack")

# 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)

# Repeat the plot in the previous cell but change palette to 'prism' and change size plt.figure (figsize = (10, 6)) sns.violinplot (x= 'Type 1', y= 'Defense')

# 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" #

# 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 #

# Set background grid for Seaborn plots

# Create scatter plot of charges vs BMI with color indiciating whether patient is # smoker or not

# Get data to use for linear regression # Right now we see if there is a relationship between insurance charges and bmi

# Make bmi an n by 1 array and charges n by 1

# Create model and fit data

# Use regplot to plot data and line

# predict insurance costs for a person with BMI 31.7; round answer to nearest cent # # Note that this value agrees with plot above because when x=31.7 y is around 14,000