# Import libraries and DataFrame
#
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
print("Hello World!")

# Read the data from pokeman.csv into a DataFrame using pandas read_csv()
# Print out the first 6 lines of data using .head
x = []
for i in range(0, 100):
print(i)
x.append(i)

# print out the data types of all features using .dtypes (no parentheses)
import matplotlib.pyplot as plt
plt.plot(x)

# print out the column names using .columns

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

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

# Make 1D NumPy arrays from the features Attack and Defense and do a scatter plot
# using matplotlib
#

# 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

# Add a white grid to the background of Seaborn plots using set_style

# Make a scatter plot using Seaborn's relplot of Defense statistics (y-axis)
# vs Attacks Stats

# Repeat plot in previous cell but use color to indicate Type 1 (hue = )

# 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)

# Make a Bar graph of Defense statistics for Type 1

# Make a violin plot of the Defense data for Type 1

# Repeat the plot in the previous cell but change palette to 'prism' and change size

# 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

# write out equation of line

# 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