diff --git a/q01_plot_deliveries_by_team/build.py b/q01_plot_deliveries_by_team/build.py
index d1dab11..974b5b6 100644
--- a/q01_plot_deliveries_by_team/build.py
+++ b/q01_plot_deliveries_by_team/build.py
@@ -1,3 +1,4 @@
+# %load q01_plot_deliveries_by_team/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -7,3 +8,24 @@
 
 
 # Solution
+def plot_deliveries_by_team():
+    
+    #create figure for drawing data
+    fig = plt.figure() 
+    
+    # group data by batting team and use count as aggregator, then select delivery column since result in the form of pandas
+    data = ipl_df.groupby('batting_team').count()['delivery']
+    
+    # plot bar graph
+    plt.bar(data.index, data.values)
+    
+    # rotate ticks of x axis by 30
+    plt.xticks(rotation = 30)
+    plt.show()
+    
+plot_deliveries_by_team()
+
+
+
+
+
diff --git a/q02_plot_matches_by_team/build.py b/q02_plot_matches_by_team/build.py
index ce53182..a440976 100644
--- a/q02_plot_matches_by_team/build.py
+++ b/q02_plot_matches_by_team/build.py
@@ -1,3 +1,4 @@
+# %load q02_plot_matches_by_team/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -6,3 +7,18 @@
 
 
 # Solution
+def plot_matches_by_team():
+    #create empty canvas to draw 
+    fig = plt.figure()
+    # groupby batting team then count unique and select match_code(as it uniquely identified matched played)
+    match_played = ipl_df.groupby('batting_team').nunique()['match_code']
+    #plotting bar
+    plt.bar(match_played.index, match_played)
+    #rotating x ticks by -90 for easy reading team name
+    plt.xticks(rotation = -90)
+    plt.show()
+    
+plot_matches_by_team()
+
+
+
diff --git a/q03_plot_innings_runs_histogram/build.py b/q03_plot_innings_runs_histogram/build.py
index ce53182..9f77f86 100644
--- a/q03_plot_innings_runs_histogram/build.py
+++ b/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 +7,35 @@
 
 
 # Solution
+def plot_inning_runs_histogram():
+    # storing first inning data
+    first_inning_data = ipl_df[ipl_df['inning'] == 1]
+    
+    #storing second inning data
+    second_inning_data = ipl_df[ipl_df['inning']==2]
+    
+    # grouping first inning data by match code and then select 'runs' columns from it 
+    first = first_inning_data.groupby('match_code').sum()['runs']
+    
+    # grouping second inning data by match code and then select 'runs' column from it
+    second = second_inning_data.groupby('match_code').sum()['runs']
+    
+    #creating figure to draw
+    fig = plt.figure()
+    
+    #creatng supplots in figure
+    fig, ax = plt.subplots(1,2)
+    
+    #drawing histogram in first subplot
+    ax[0].hist(first)
+    
+    #drawing histogram in second subplot
+    ax[1].hist(second)
+    
+    plt.show()
+    
+
+
+plot_inning_runs_histogram()
+
+
diff --git a/q04_plot_runs_by_balls/build.py b/q04_plot_runs_by_balls/build.py
index ce53182..def5e07 100644
--- a/q04_plot_runs_by_balls/build.py
+++ b/q04_plot_runs_by_balls/build.py
@@ -1,3 +1,4 @@
+# %load q04_plot_runs_by_balls/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -6,3 +7,24 @@
 
 
 # Solution
+def plot_runs_by_runs():
+    # number of balls played by players
+    no_of_balls = ipl_df.groupby('batsman').count()['delivery']
+    # total runs made by players
+    runs = ipl_df.groupby('batsman', sort = True).sum()['runs']
+    #creating figure for plotting data
+    fig = plt.figure()
+    #using scatter function to show relationship between no of balls and runs
+    plt.scatter(no_of_balls, runs)
+    
+    plt.xlabel('balls playesd')
+    plt.ylabel('runs scored')
+    
+    plt.show()
+plot_runs_by_runs()
+
+
+
+
+
+