learn-co-students / 011121_one_one_visualization

Data Visualization

In this checkpoint you will be using a FIFA dataset of soccer player statistics to create a visualization, then also interpreting a visualization we have created.

First, we import the relevant libraries

# Run this cell without changes
import csv
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# __SOLUTION__
import csv
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Next, we read data from a CSV and create player_nationalities, a list of dictionaries containing data about the number of FIFA soccer players from each country. The countries are in random order.

This code also prints the first five dictionaries in player_nationalities, out of a total of 161

# Run this cell without changes
player_nationalities = []
with open("data/player_nationalities.csv") as f:
    for row in csv.DictReader(f):
        row['count_of_players'] = int(row['count_of_players'])
        player_nationalities.append(dict(row))

for index in range(5):
    print(player_nationalities[index])

# __SOLUTION__
player_nationalities = []
with open("data/player_nationalities.csv") as f:
    for row in csv.DictReader(f):
        row['count_of_players'] = int(row['count_of_players'])
        player_nationalities.append(dict(row))
for index in range(5):
    print(player_nationalities[index])

{'country': 'Afghanistan', 'count_of_players': 4}
{'country': 'Croatia', 'count_of_players': 113}
{'country': 'Benin', 'count_of_players': 15}
{'country': 'Azerbaijan', 'count_of_players': 5}
{'country': 'Montenegro', 'count_of_players': 20}

1a) Find the top 10 countries with the most players.

#Your answer here

# __SOLUTION__
ordered = sorted(player_nationalities, key=lambda x: x['count_of_players'], reverse=True)
top10 = ordered[:10]
top10

1b) Create a bar chart showing the number of players in those 10 countries

Don't forget to add a title and y axis label to your charts.

A recommended figsize is (10, 6)

If you are unable to find the top 10 countries but want the chance to demonstrate your plotting skills use the first 10 randomly ordered countries: top10 = player_nationalities[:10]

#Your answer here

# __SOLUTION__
fig, ax = plt.subplots(figsize=(10, 6))

ax.set_title("10 Countries with the Most Players")
ax.set_ylabel("Number of Players")

labels = [row['country'] for row in top10]
values = [row['count_of_players'] for row in top10]
ax.bar(labels, values);

2) Below is code for a scatter plot for the player stats Standing Tackle and Sliding Tackle

# Run this cell without changes
player_stats = []
with open("data/player_stats.csv") as f:
    for row in csv.DictReader(f):
        row["standing_tackle"] = float(row["standing_tackle"])
        row["sliding_tackle"] = float(row["sliding_tackle"])
        player_stats.append(dict(row))
        
for index in range(5):
    print(player_stats[index])

# __SOLUTION__
player_stats = []
with open("data/player_stats.csv") as f:
    for row in csv.DictReader(f):
        row["standing_tackle"] = float(row["standing_tackle"])
        row["sliding_tackle"] = float(row["sliding_tackle"])
        player_stats.append(dict(row))
for index in range(5):
    print(player_stats[index])

{'standing_tackle': 28.0, 'sliding_tackle': 26.0}
{'standing_tackle': 31.0, 'sliding_tackle': 23.0}
{'standing_tackle': 24.0, 'sliding_tackle': 33.0}
{'standing_tackle': 21.0, 'sliding_tackle': 13.0}
{'standing_tackle': 58.0, 'sliding_tackle': 51.0}

# Run this cell without changes
fig, ax = plt.subplots(figsize=(8, 8))

ax.set_title('Standing Tackle vs. Sliding Tackle')
ax.set_xlabel('Standing Tackle')
ax.set_ylabel('Sliding Tackle')

x = [row["standing_tackle"] for row in player_stats]
y = [row["sliding_tackle"] for row in player_stats]

ax.scatter(x, y, alpha=0.2);

# __SOLUTION__
fig, ax = plt.subplots(figsize=(8, 8))
ax.set_title('Standing Tackle vs. Sliding Tackle')
ax.set_xlabel('Standing Tackle')
ax.set_ylabel('Sliding Tackle')
x = [row["standing_tackle"] for row in player_stats]
y = [row["sliding_tackle"] for row in player_stats]
ax.scatter(x, y, alpha=0.2);

How would you describe the relationship between these two features?

#Your answer here

# __SOLUTION__
'''These two features are highly correlated.'''