6  Creating Visualisation with Python

7 Analyse Shehei Ohtani’s Performance

In this chapter, we want to explore some common charts using Python. We will continue to use Pandas whenever we can.

import pandas as pd
import sqlite3

# Use 'with' to ensure the connection is properly closed after use
with sqlite3.connect("resources/mlb2023.db") as conn:
    # Write your SQL query
    query = "SELECT * FROM batting"
    
    # Read data from the database into a pandas DataFrame
    mlb = pd.read_sql_query(query, conn)


with sqlite3.connect("resources/mlbpost2023.db") as conn:
    # Write your SQL query
    query = "SELECT * FROM batting"
    
    # Read data from the database into a pandas DataFrame
    mlbpost = pd.read_sql_query(query, conn)
# show first few rows
mlb.head()
playerID yearID stint teamID lgID G G_batting AB R H ... SB CS BB SO IBB HBP SH SF GIDP G_old
0 aardsda01 2004 1 SFN NL 11 0 0 0 ... 0 0 0 0 0 0 0 0 0
1 aardsda01 2006 1 CHN NL 45 2 0 0 ... 0 0 0 0 0 0 1 0 0
2 aardsda01 2007 1 CHA AL 25 0 0 0 ... 0 0 0 0 0 0 0 0 0
3 aardsda01 2008 1 BOS AL 47 1 0 0 ... 0 0 0 1 0 0 0 0 0
4 aardsda01 2009 1 SEA AL 73 0 0 0 ... 0 0 0 0 0 0 0 0 0

5 rows × 24 columns

# Shohei Ohtani's playerID is: ohtansh01
filter = mlb["playerID"] == "ohtansh01"
ohtani = mlb[filter]
75963    22
75964    18
75965     7
75966    46
75967    34
75968    44
Name: HR, dtype: int64

# Show all the columns (fields)
ohtani.columns
Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'G_batting', 'AB',
       'R', 'H', '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP',
       'SH', 'SF', 'GIDP', 'G_old'],
      dtype='object')

7.1 Bar Chart

ohtani[["yearID", "HR"]]
yearID HR
75963 2018 22
75964 2019 18
75965 2020 7
75966 2021 46
75967 2022 34
75968 2023 44 
ohtani.plot(x="yearID", y="HR", kind="bar", legend=False, figsize=(4,3))

# Add chart details
plt.title("Shohei Ohtani's Home Runs by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Home Runs", fontsize=12)

# Display the plot
plt.show()

ohtani.plot(x="yearID", y="HR", kind="barh", legend=False, figsize=(4,3))

# Add chart details
plt.title("Shohei Ohtani's Home Runs by Year", fontsize=14)
plt.xlabel("Home Runs", fontsize=12)
plt.ylabel("Year", fontsize=12)


# Display the plot
plt.show()

ohtani.plot(x="yearID", y=["HR", "RBI"], kind="bar", legend=False, figsize=(4,3))

# Add chart details
plt.title("Shohei Ohtani's Home Runs and RBI by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Home Runs", fontsize=12)

# Display the plot
plt.show()

7.2 Line Graph

ohtani[["yearID", "HR", "SB"]]
yearID HR SB
75963 2018 22 10
75964 2019 18 12
75965 2020 7 7
75966 2021 46 26
75967 2022 34 11
75968 2023 44 20 
ohtani.plot(x="yearID", y="HR", figsize=(4,3))

# Add chart details
plt.title("Shohei Ohtani's Home Runs by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Home Runs", fontsize=12)

# Display the plot
plt.show()

ohtani.plot(x="yearID", y=["HR", "SB"], figsize=(4,3))

# Add chart details
plt.title("Shohei Ohtani's Home Runs and Stolen Bases by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Home Runs", fontsize=12)

# Display the plot
plt.show()

7.3 Pie Graph

ohtani.set_index('yearID')['HR']
yearID
2018    22
2019    18
2020     7
2021    46
2022    34
2023    44
Name: HR, dtype: int64
ohtani.set_index('yearID')['HR'].plot(kind="pie")

# Add chart details
plt.title("Shohei Ohtani's Home Runs by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)

# Display the plot
plt.show()

ohtani.set_index('yearID')['HR'].plot(kind="pie", autopct='%1.1f%%')

# Add chart details
plt.title("Shohei Ohtani's Home Runs by Year", fontsize=14)
plt.xlabel("Year", fontsize=12)

# Display the plot
plt.show()

7.4 Advanced Statistics

ohtani2 = ohtani.copy()
ohtani[["yearID", "AB", "H", "2B", "3B", "HR", "BB"]]
ohtani2["AVG"] = ohtani["H"] / ohtani["AB"]

7.5 Histogram

angels_2023 = mlb[(mlb["yearID"] == 2023) & (mlb["teamID"] == "LAA") & (mlb["AB"] != 0)]
angels2023[["AB", "H", "HR"]]
AB H HR
506 39 5 0
641 58 12 3
14384 53 11 1
22036 50 10 1
27498 485 127 26
29746 178 39 2
32202 89 22 2
39328 194 42 8
56909 51 11 2
70478 311 87 14
72090 237 56 8
73970 289 65 9
75957 182 43 14
75968 497 151 44
76017 2 0 0
77621 8 1 0
78097 40 4 0
80689 63 11 3
84995 148 35 2
85004 459 111 19
85012 394 104 16
91314 109 30 1
96644 9 2 0
97961 62 18 0
101636 262 56 9
103699 308 81 18
104500 214 64 2
105414 81 14 2
106998 157 31 7
107153 104 13 4
107485 356 90 14 
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

# Calculate the number of bins based on the data range and desired bin width
bin_width = 5
min_hr = 0
max_hr = angels_2023["HR"].max()
bins = range(0, int(max_hr) + bin_width, bin_width)

# Plot the histogram with specified bin width
angels_2023["HR"].plot(kind="hist", bins=bins)

# Add chart details
plt.title("Angels' Home Runs in 2023", fontsize=14)
plt.xlabel("Home Runs", fontsize=12)
plt.ylabel("Player Number", fontsize=12)

# Ensure y-axis has whole number ticks
plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))

# Show the grid
plt.grid(True)

# Display the plot
plt.show()

# show first few rows
mlbpost.head()
yearID round playerID teamID lgID G AB R H 2B ... RBI SB CS BB SO IBB HBP SH SF GIDP
0 1884 WS becanbu01 NY4 AA 1 2 0 1 0 ... 0 0 0 0 0
1 1884 WS bradyst01 NY4 AA 3 10 1 0 0 ... 0 0 0 1 0
2 1884 WS carrocl01 PRO NL 3 10 2 1 0 ... 1 0 1 1 0
3 1884 WS dennyje01 PRO NL 3 9 3 4 0 ... 2 0 0 3 0
4 1884 WS esterdu01 NY4 AA 3 10 0 3 1 ... 0 1 0 3 0

5 rows × 22 columns

mlbpost.columns
Index(['yearID', 'round', 'playerID', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',
       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',
       'SF', 'GIDP'],
      dtype='object')