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q02_rename_columns/build.py

@@ -1,9 +1,17 @@
+# %load q02_rename_columns/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 from greyatomlib.olympics_project.q01_load_data.build import q01_load_data
 
 def q02_rename_columns(path):
-    "write your solution here"
+    'write your solution here'
     df = q01_load_data(path)
-
+    df = pd.DataFrame(df)
+    df.rename(columns={'01 !': 'Gold' , '02 !': 'Silver','03 !': 'Bronze'}, inplace=True)
+    return df
+
+q02_rename_columns('./data/olympics.csv')
+
+
+

q03_split_country/build.py

@@ -1,10 +1,45 @@
+# %load q03_split_country/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 from greyatomlib.olympics_project.q02_rename_columns.build import q02_rename_columns
+import re
 
+path = './data/olympics.csv'
+df_new = []
 
 def q03_summer_gold_medals(path):
-    "write your solution here"
+    'write your solution here'
     df = q02_rename_columns(path)
-
+    #df = pd.DataFrame(df, index = 'country')
+
+    country = []
+    code = []
+
+    for name in df['country name']:
+        country.append((name.split()[0]))
+        code.append(''.join(re.findall('\(\w{3}\)', name)))
+
+    df.index = country
+    df.drop(labels = ['Combined total'], axis = 1, inplace = True)
+    df.drop(labels = ['Totals'], axis = 0, inplace = True)
+    #for name in df['country name']:
+    #    country.append((name.split()[0], ''.join(re.findall('\(\w{3}\)', name))))
+    #for i in range(1,lene.(df)+1):
+    return df
+
+        #df_new.append(df.loc[i,'country name'].split())
+        #country = df.loc[i, 'country name'].split()
+   #print(df.loc[i, 'country name'].str.contains('\(\w{3}\)'))
+
+
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