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q01_read_csv_data_to_df/build.py

@@ -1,8 +1,14 @@
+# %load q01_read_csv_data_to_df/build.py
 # Default Imports
 import pandas as pd
 
 # Path has been given to you already to use in function.
-path = "data/ipl_dataset.csv"
+path = 'data/ipl_dataset.csv'
 
 # Solution
+def read_csv_data_to_df (path) : 
+    df = pd.read_csv(path)
+    return df
+df = read_csv_data_to_df (path)
+df.shape
 

q02_get_unique_values/build.py

@@ -1,6 +1,15 @@
+# %load q02_get_unique_values/build.py
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
 
 # You have been given the dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('data/ipl_dataset.csv')
 
 #Solution
+def get_unique_venues() :
+    uni = ipl_df['venue'].unique()
+    return uni
+ipl_df.head()
+ipl_df['venue'].unique()
+
+
+

q03_get_run_counts/build.py

@@ -1,8 +1,17 @@
+# %load q03_get_run_counts/build.py
 # Default Imports
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
 
 # You have been given the dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv')
 
 # Solution
+def get_run_counts() :
+    for x in ipl_df['runs']:
+        freq = ipl_df['runs'].value_counts()
+    return freq
+
+#ipl_df['runs']
+get_run_counts()
+
 

q04_get_match_specific_df/build.py

@@ -1,7 +1,20 @@
+# %load q04_get_match_specific_df/build.py
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
-
+import pandas as pd
 # You have been given dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv')
 
 # Solution
+def get_match_specific_df(match_code):
+    s = pd.Series()
+    s = ipl_df.loc[ipl_df['match_code'] == match_code]
+    return s
+type(ipl_df['match_code'])
+ipl_df.loc[ipl_df['match_code'] == 392203]
+
+get_match_specific_df(598057)
+
+
+
+
 

q05_create_bowler_filter/build.py

@@ -1,7 +1,51 @@
+# %load q05_create_bowler_filter/build.py
 # Default imports
+import pandas as pd
+import numpy as np
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
 
 # You have been given dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv')
 
 # Solution
+
+
+
+def create_bowler_filter (name):
+    for x in ipl_df['bowler']:
+        #s = pd.Series( index = list(x))
+        s = pd.Series(ipl_df['bowler'] == name)
+        #logical_results = str(s) == name
+        #logical_results_series = pd.Series(np.arange(0,8),index=logical_results)
+        #new = s[logical_results]
+        #return new
+        return s
+    #bow_array =pd.Series
+#for x in ipl_df['bowler']:
+ #ipl_df['bowler'] == name
+  #bow_array = pd.Series(True)
+   # return bow_array
+create_bowler_filter('I Sharma').shape
+
+#if ipl_df['bowler'] == 'AB Dinda':
+ #   boolean_value = True
+#else:
+  #  boolean_value = False
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

q06_get_match_innings_runs/build.py

@@ -1,11 +1,16 @@
+# %load q06_get_match_innings_runs/build.py
 # Default Imports
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
 
 # You have been given dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('data/ipl_dataset.csv')
 
-# Solution
+## Solution
+def get_match_innings_runs():
+    result = ipl_df.groupby(['match_code','inning'])['runs'].sum()
+    return result
 
 
 
+get_match_innings_runs()
 

q07_get_run_counts_by_match/build.py

@@ -1,7 +1,18 @@
+# %load q07_get_run_counts_by_match/build.py
 # Default Imports
 from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df
-
+import pandas as pd
+import numpy as np
 # You have been give the dataset already in 'ipl_df'.
-ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv")
+ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv')
 
 # Solution
+
+def get_runs_counts_by_match():
+    runs_by_match = ipl_df.pivot_table(values='delivery', index='match_code', columns=['runs'],
+                                       aggfunc='count')
+    return runs_by_match
+get_runs_counts_by_match()
+
+
+