# 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
for i in range(0, 100):
print(i)

```
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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31
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43
44
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46
47
48
49
50
51
52
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62
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68
69
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91
92
93
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95
96
97
98
99
```

# print out the data types of all features using .dtypes (no parentheses)

# 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

# Read the data from pokeman.csv into a DataFrame using pandas read_csv()
# Print out the first 6 lines of data using .head

# print out the data types of all features using .dtypes (no parentheses)

# 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
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