Week 10 Practice Notebooks

# 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)

#int64

Nameobject

Bulbasaur

Ivysaur

Venusaur

Charmander

Charmeleon

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"]

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

[ 45  60  80  65  80 100  43  58  78  45  30  70  50  35  75  56  71 101
  72  97  70 100  55  80  90 110  40  65  41  56  76  50  65  85  35  60
  65 100  20  45  55  90  30  40  50  25  30  45  90  95 120  90 115  55
  85  70  95  60  95  90  90  70  90 105 120  35  45  55  40  55  70  70
 100  20  35  45  90 105  15  30  45  70  60  75 100  45  70  25  50  40
  70  80  95 110  70  42  67  50  75 100 140  40  55  35  45  87  76  30
  35  60  25  40  50  60  90  60  85  63  68  85 115  90 105  95 105  93
  85 110  80  81  60  48  55  65 130  65  40  35  55  55  80 130  30  85
 100  90  50  70  80 130 100]

# Make 1D NumPy arrays from the features Attack and Defense and do a scatter plot # using matplotlib Att = df.Attack.values Def = df.Defense.values plt.scatter(Att, Def) plt.xlabel("Attack") plt.ylabel("Defense") plt.title("Attack vs. Defense")

# 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%

Bulbasaur

Ivysaur

Venusaur

Charmander

Charmeleon

Charizard

Squirtle

Wartortle

Blastoise

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 # The section instructions say the first 6 lines of data, like the first section, so I'm going with 6 just in case! df = pd.read_csv("pokeman.csv") df.head(6)

#int64

Nameobject

Bulbasaur

Ivysaur

Venusaur

Charmander

Charmeleon

Charizard

# Add a white grid to the background of Seaborn plots using set_style sns.set_style("whitegrid") #Don't capitalize the W, it won't accept it!

# Make a scatter plot using Seaborn's relplot of Defense statistics (y-axis) # vs Attacks Stats sns.relplot(x = "Attack", y = "Defense", data = df) # Have to be very specific unlike with previous scatter plot code

# Repeat plot in previous cell but use color to indicate Type 1 (hue = ) sns.relplot(x = "Attack", y = "Defense", data = df, hue = "Type 1") # Just add on the hue part to the code from previous box

# 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) # Category, so use catplot # Wasn't sure what data should be on which axis so I went with what I thought made more sense sns.catplot(x = "Type 1", y = "Defense", data = df, hue = "Type 1") plt.xticks(rotation = -45)

# Make a Bar graph of Defense statistics for Type 1 # Use barplot, continue being specific when entering data! 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", height = 10) plt.xticks(rotation = -45) # I didn't notice any differences when changing size so I may have done it wrong

# 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" #(1) plt.figure(figsize = (10, 6)) #(2) sns.violinplot(x = "Type 1", y = "Defense", data = df, inner = None) #(3) plt.xticks(rotation = -45) #(4) Basically copy violinplot but change from violin to swarm sns.swarmplot(x = "Type 1", y = "Defense", data = df, color = "k") #(5) plt.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 df = pd.read_csv("insurance.csv") df.head(6)

ageint64

sexobject

female

male

female

# 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(x = "bmi", y = "charges", data = df, hue = "smoker") # Don't capitalize "charges" or "smoker", won't work then bc it doesn't specifically match insurance.csv

# 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 lr_int = lr.intercept_[0] lr_sl = lr.coef_[0,0] print("The intercept is", lr_int) print("The slope is", lr_sl)

The intercept is 1192.9372089611497
The 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 # Note that this value agrees with plot above because when x=31.7 y is around 14,000 x_eval = np.array([31.7]) x_eval = np.reshape(x_eval, (1, 1)) print("The predicted insurance costs for a person with BMI 31.7 is", lr.predict(x_eval))

The predicted insurance costs for a person with BMI 31.7 is [[13678.71228524]]