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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
+# %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
+
 

q02_get_unique_values/build.py

@@ -1,6 +1,12 @@
+#%load q02_get_unique_values/build.py
+# %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():
+    venues = ipl_df['venue'].unique()
+    return venues
+

q03_get_run_counts/build.py

@@ -1,8 +1,18 @@
+#%load q03_get_run_counts/build.py
+# %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
 
+import pandas as pd
 # 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
+l=[]
+# Solution
+def get_run_counts():
+    l=ipl_df['runs'].value_counts()
+    return pd.Series(l)
+get_run_counts()
+
 

q04_get_match_specific_df/build.py

@@ -1,7 +1,15 @@
+#%load q04_get_match_specific_df/build.py
+# %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
 
 # 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')
+(ipl_df['match_code'])
 # Solution
+def get_match_specific_df(mc):
+    df=ipl_df[(ipl_df['match_code']==mc)]
+    return df
+get_match_specific_df(598057)
+
+
 

q05_create_bowler_filter/build.py

@@ -1,7 +1,14 @@
+#%load q05_create_bowler_filter/build.py
+# %load q05_create_bowler_filter/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')
+import pandas as pd
 # Solution
+def create_bowler_filter(b):
+    a=pd.Series(ipl_df['bowler']==b)
+    return a
+create_bowler_filter('I Sharma')
+

q06_get_match_innings_runs/build.py

@@ -1,11 +1,29 @@
+#%load q06_get_match_innings_runs/build.py# %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')
 
+import numpy as np
 # Solution
+def get_match_innings_runs():
+    a=ipl_df.groupby(by=['match_code','inning']).agg(np.sum)
+    return a[['runs']]
+get_match_innings_runs()
 
+# %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')
+
+import numpy as np
+# Solution
+def get_match_innings_runs():
+    a=ipl_df.groupby(by=['match_code','inning']).agg(np.sum)
+    return a[['runs']]
+get_match_innings_runs()
 
 

q07_get_run_counts_by_match/build.py

@@ -1,7 +1,17 @@
+#%load q07_get_run_counts_by_match/build.py
+# %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
 
 # 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
+import numpy as np
+r=ipl_df['runs'].unique()
+def get_runs_counts_by_match():
+    a=ipl_df.pivot_table(index='match_code',columns='runs',aggfunc='count')
+    return a['batsman']
+get_runs_counts_by_match()
+
+