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q01_get_total_deliveries_players/build.py

@@ -1,7 +1,22 @@
+# %load q01_get_total_deliveries_players/build.py
 # Default imports
 import numpy as np
 
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=0, delimiter=',')
 
 # Your Solution
+def get_total_deliveries_played(batsman):
+
+    #Storing the column of batsman into a separate list
+    batsman_list = ipl_matches_array[1:,13]
+
+    #Returning the count
+    return np.count_nonzero(batsman_list==batsman)
+
+#Call to the function.
+get_total_deliveries_played(b'SR Tendulkar')
+
+
+
+
 

q02_get_wicket_delivery_numbers_array/build.py

@@ -1,7 +1,25 @@
+# %load q02_get_wicket_delivery_numbers_array/build.py
 #Default Imports
 import numpy as np
 
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=0, delimiter=',')
 
 #Your Solution
+def get_wicket_delivery_numbers_array(player):
+
+    # We'll make use of boolean indexing to fetch the desired result.
+    # batsman_out would store all the row values which have to be fetched.
+    batsman_out = ipl_matches_array[:,-3]==player
+
+    #As we have to fetch the delivery, we put 11 in the coulmn and batsman_out in row.
+    return ipl_matches_array[batsman_out,11]
+
+
+#Call to the function - 
+get_wicket_delivery_numbers_array(b'SR Tendulkar')
+
+
+
+
+
 

q03_get_toss_win_count/build.py

@@ -1,7 +1,24 @@
+# %load q03_get_toss_win_count/build.py
 #Default Imports
 import numpy as np
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=0, delimiter=',')
 
 
 #Your Solution
+def get_toss_win_count(team):
+
+    #Boolean indexing to store toss wins for MI.
+    MI_TossWin = ipl_matches_array[:,5]==team
+
+    #getting unique toss wins based on match id as the value gets repeated per delivery.
+    unique_win_count = np.unique(ipl_matches_array[MI_TossWin,0])
+
+    #returning the count.
+    return np.count_nonzero(unique_win_count)
+
+get_toss_win_count(b'Mumbai Indians')
+
+
+
+
 

q04_get_all_sixes_filter/build.py

@@ -1,7 +1,16 @@
+# %load q04_get_all_sixes_filter/build.py
 #Default Imports
 import numpy as np
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=1, delimiter=',')
 
 
 #Your Solution
+def get_all_sixes_filter():
+    #Returning boolean index array where element value for runs is 6.
+    return ipl_matches_array[:,-7]==b'6'
+
+#Call to the function
+get_all_sixes_filter()
+
+
 

q05_create_delivery_series/build.py

@@ -1,7 +1,20 @@
+# %load q05_create_delivery_series/build.py
 #Default Imports
 import pandas as pd
 import numpy as np
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=1, delimiter=',')
 
 #Your Solution
+def create_delivery_series():
+
+    #Pikcing up the column of delivery and passing it to series.
+    Ser1 = pd.Series(ipl_matches_array[:,11])
+    return Ser1
+
+#Call to the function
+create_delivery_series()
+
+
+
 

q06_create_runs_series/build.py

@@ -1,7 +1,26 @@
+# %load q06_create_runs_series/build.py
 #Default Imports
 import pandas as pd
 import numpy as np
-ipl_matches_array =np.genfromtxt("data/ipl_matches_small.csv", dtype="|S50", skip_header=1, delimiter=",")
+ipl_matches_array =np.genfromtxt('data/ipl_matches_small.csv', dtype='|S50', skip_header=1, delimiter=',')
 
 #Your Solution
+def create_runs_series(match_code):
+
+    #Assigning a variable to data and index column.
+    data = ipl_matches_array[:,16]
+    index = ipl_matches_array[:,11]
+
+    #Returning the Pandas Series.
+    return pd.Series(data,index)
+
+#Call to the function.
+create_runs_series(b'392203')
+
+
+
+
+
+
+