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## Introduction to Python

Run the hidden code cell below to import the data used in this course.

```
# Importing course packages; you can add more too!
import numpy as np
import math
# Import columns as numpy arrays
baseball_names = np.genfromtxt(
fname="baseball.csv", # This is the filename
delimiter=",", # The file is comma-separated
usecols=0, # Use the first column
skip_header=1, # Skip the first line
dtype=str, # This column contains strings
)
baseball_heights = np.genfromtxt(
fname="baseball.csv", delimiter=",", usecols=3, skip_header=1
)
baseball_weights = np.genfromtxt(
fname="baseball.csv", delimiter=",", usecols=4, skip_header=1
)
baseball_ages = np.genfromtxt(
fname="baseball.csv", delimiter=",", usecols=5, skip_header=1
)
soccer_names = np.genfromtxt(
fname="soccer.csv",
delimiter=",",
usecols=1,
skip_header=1,
dtype=str,
encoding="utf",
)
soccer_ratings = np.genfromtxt(
fname="soccer.csv",
delimiter=",",
usecols=2,
skip_header=1,
encoding="utf",
)
soccer_positions = np.genfromtxt(
fname="soccer.csv",
delimiter=",",
usecols=3,
skip_header=1,
encoding="utf",
dtype=str,
)
soccer_heights = np.genfromtxt(
fname="soccer.csv",
delimiter=",",
usecols=4,
skip_header=1,
encoding="utf",
)
soccer_shooting = np.genfromtxt(
fname="soccer.csv",
delimiter=",",
usecols=8,
skip_header=1,
encoding="utf",
)
```

### Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

*Add your notes here*

`# Add your code snippets here`

### Explore Datasets

Use the arrays imported in the first cell to explore the data and practice your skills!

- Print out the weight of the first ten baseball players.
- What is the median weight of all baseball players in the data?
- Print out the names of all players with a height greater than 80 (heights are in inches).
- Who is taller on average? Baseball players or soccer players? Keep in mind that baseball heights are stored in inches!
- The values in
`soccer_shooting`

are decimals. Convert them to whole numbers (e.g., 0.98 becomes 98). - Do taller players get higher ratings? Calculate the correlation between
`soccer_ratings`

and`soccer_heights`

to find out! - What is the average rating for attacking players (
`'A'`

)?

```
#1
baseball_weights[:10]
```

```
#2
print(np.median(baseball_weights))
```

```
#3
baseball_names[baseball_heights>80]
```

```
#4
a=np.mean(soccer_heights)
b=np.mean(baseball_heights*2.54)
c=[a if a>b else b]
print(c)
```

```
#5
soccer_shooting*100
```

```
#6
np.corrcoef(soccer_heights,soccer_ratings)
```

```
#7
np.mean(soccer_ratings[soccer_positions=='A'])
```

```
#Just to check the data
import pandas as pd
df=pd.read_csv('baseball.csv')
df
```