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q01_plot_deliveries_by_team/build.py

@@ -1,9 +1,19 @@
+# %load q01_plot_deliveries_by_team/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 
+
 ipl_df = pd.read_csv('data/ipl_dataset.csv', index_col=None)
 
 
 # Solution
+def plot_deliveries_by_team(ipl_df=ipl_df):
+    ipl_df['batting_team'].value_counts().plot.bar()
+plot_deliveries_by_team()
+
+
+
+
+

q02_plot_matches_by_team/build.py

@@ -1,8 +1,16 @@
+# %load q02_plot_matches_by_team/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 ipl_df = pd.read_csv('data/ipl_dataset.csv', index_col=None)
 
+def plot_matches_by_team():
+    ipl_df.groupby('batting_team').agg({'match_code': pd.Series.nunique}).plot.bar()
+
+plot_matches_by_team()
 
 # Solution
+
+
+

q03_plot_innings_runs_histogram/build.py

@@ -1,3 +1,4 @@
+# %load q03_plot_innings_runs_histogram/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -6,3 +7,16 @@
 
 
 # Solution
+def plot_innings_runs_histogram():
+    inning_wise_runs = ipl_df.groupby(['match_code', 'inning'])['runs'].sum()
+    first_inning_runs = inning_wise_runs[:,1]
+    second_inning_runs = inning_wise_runs[:,2]
+    fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
+
+    axs[0].hist(first_inning_runs)
+    axs[1].hist(second_inning_runs)
+
+    return
+
+
+

q04_plot_runs_by_balls/build.py

@@ -1,8 +1,19 @@
+# %load q04_plot_runs_by_balls/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 ipl_df = pd.read_csv('data/ipl_dataset.csv', index_col=None)
 
+def plot_runs_by_balls():
+    balls_played_by_player = ipl_df.groupby(['match_code','batsman'])['delivery'].agg('count')
+    runs_made_by_player = ipl_df.groupby(['match_code','batsman'])['runs'].agg('sum')
+    plt.scatter(balls_played_by_player, runs_made_by_player)
+
+    return
 
 # Solution
+
+
+
+