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q01_zeros_array/build.py

@@ -1,8 +1,15 @@
+# %load q01_zeros_array/build.py
 # Default Imports
 import sys, os
 sys.path.append(os.path.join(os.path.dirname(os.curdir),  '..'  ))
-import numpy as np
 
 # Your solution
 
+import numpy as np
+def array_zeros():
+    zeros_array = np.zeros(((3,4,2)))
+    return zeros_array
+
+
+
 

q02_zeros_reshaped/build.py

@@ -1,5 +1,15 @@
+# %load q02_zeros_reshaped/build.py
 # Default imports
 import numpy as np
 from greyatomlib.python_intermediate.q01_zeros_array.build import  array_zeros
 
 # Write your code
+
+zeros_array = np.zeros(((3,4,2)))
+
+def array_reshaped_zeros():
+    zeros_array_reshaped = zeros_array.reshape(((2,3,4)))
+    return zeros_array_reshaped
+
+
+

q03_create_3d_array/build.py

@@ -1,4 +1,21 @@
+# %load q03_create_3d_array/build.py
 # Default Imports
+
+# Enter solution here
 import numpy as np
 
-# Enter solution here
+def create_3d_array():
+    N = 3*3*3
+
+    numbers = []
+    for x in range(0,N):
+        numbers.append(x)
+
+    array_number = np.array(numbers)
+    array_number = array_number.reshape(3,3,3)
+
+    return array_number
+
+
+
+

q04_read_csv_data_to_ndarray/build.py

@@ -1,5 +1,17 @@
-# Default Imports
+# %load q04_read_csv_data_to_ndarray/build.py
+
+
+import csv 
 import numpy as np
-path = "./data/ipl_matches_small.csv"
 
-# Enter code here
+def read_csv_data_to_ndarray(path , dtype):
+
+    with open(path, 'r') as f:
+        matches = list(csv.reader(f, delimiter=','))
+
+    ipl_matches = np.array(matches[1:] , dtype=dtype)
+    return ipl_matches
+
+
+
+

q05_read_csv_data/build.py

@@ -1,4 +1,31 @@
+# %load q05_read_csv_data/build.py
 # Default imports
+from greyatomlib.python_intermediate.q05_read_csv_data.build import read_ipl_data_csv
+path = 'data/ipl_matches_small.csv'
 import numpy as np
+import pandas as pd
+
+def get_unique_matches_count():
+
+    no_of_matches_played = 0
+    with open(path,'r') as csvfile:
+        csvreader = csv.reader(csvfile)
+        data = [row for row in csvreader] 
+        np_ipl_data = np.asarray(data)
+
+    headers = tuple(np_ipl_data[0])
+
+    #slicing main ndarray into required ndarray
+    match_codes = np_ipl_data[1:,[headers.index('match_code')]]
+
+    #reshaping ndarray from 2d to 1d to make a list
+    match_codes_array = match_codes.reshape(np.product(match_codes.shape))
+
+    #converting 1d array to a list and then removing duplicates to get unique values
+    match_codes_set = set(match_codes_array.tolist())
+
+    #length of the set will be the total number of matches played
+    no_of_matches_played = len(match_codes_set)
+    return no_of_matches_played
+
 
-# Enter code here

q06_get_unique_matches_count/build.py

@@ -1,5 +1,33 @@
+# %load q06_get_unique_matches_count/build.py
 # Default imports
 from greyatomlib.python_intermediate.q05_read_csv_data.build import read_ipl_data_csv
 path = 'data/ipl_matches_small.csv'
 
 # Enter Code Here
+import numpy as np
+import csv
+
+def get_unique_matches_count():
+
+    no_of_matches_played = 0
+    with open(path,'r') as csvfile:
+        csvreader = csv.reader(csvfile)
+        data = [row for row in csvreader] 
+        np_ipl_data = np.asarray(data)
+
+    headers = tuple(np_ipl_data[0])
+
+    #slicing main ndarray into required ndarray
+    match_codes = np_ipl_data[1:,[headers.index('match_code')]]
+
+    #reshaping ndarray from 2d to 1d to make a list
+    match_codes_array = match_codes.reshape(np.product(match_codes.shape))
+
+    #converting 1d array to a list and then removing duplicates to get unique values
+    match_codes_set = set(match_codes_array.tolist())
+
+    #length of the set will be the total number of matches played
+    no_of_matches_played = len(match_codes_set)
+    return no_of_matches_played
+
+

q07_get_unique_teams_set/build.py

@@ -1,5 +1,30 @@
+# %load q07_get_unique_teams_set/build.py
 # Default imports
 from greyatomlib.python_intermediate.q05_read_csv_data.build import read_ipl_data_csv
-path = "data/ipl_matches_small.csv"
 
-# Enter Code Here
+path = 'data/ipl_matches_small.csv'
+import numpy as np
+
+def get_unique_teams_set():
+
+    np_ipl_data = read_ipl_data_csv(path , '|S50')
+
+    #team 1 2d array
+    team_1 = np_ipl_data[1:,[3]]
+
+    #reshaping to 1d array
+    team_1_1d = team_1.reshape(np.product(team_1.shape))
+
+    #team 1 2d array
+    team_2 = np_ipl_data[1:,[4]]
+
+    #reshaping to 1d array
+    team_2_1d = team_2.reshape(np.product(team_2.shape))
+
+    #taking union of two lists to remove duplicates and merge two lists in one list
+    names_of_teames_played = set().union(team_1_1d,team_2_1d)
+
+    return names_of_teames_played
+
+
+

q08_get_total_extras/build.py

@@ -1,7 +1,20 @@
+# %load q08_get_total_extras/build.py
 # Default Imports
 from greyatomlib.python_intermediate.q05_read_csv_data.build import read_ipl_data_csv
 import numpy as np
 
 path = 'data/ipl_matches_small.csv'
 
-# Enter Code Here
+# Enter Code Here
+
+def get_total_extras():
+    np_ipl_data =  read_ipl_data_csv(path , '|S50')
+    #creating tuple for headers
+
+    extras = np_ipl_data[:,[17]]
+    extras_list = extras.reshape(np.product(extras.shape)).tolist()
+    extras_in = [int(x) for x in extras_list]
+    return sum(extras_in)
+
+
+