Completes Task/07

task-07/get_top_names.py

@@ -6,11 +6,32 @@
 
 import pandas as pd
 
+
 def extract_data_lines(filename, start_text, end_text, include_start=False, include_end=False):
+    """
+    open `filename`, and yield the lines between
+    the line that contains `start_text` and the line that contains `end_text`
+    """
     # Add your code for Step 1 here
     # Just copy your answer from task-03
     # Remove the raise statement when you are done
-    raise NotImplementedError
+
+    with open(filename) as fh:
+        in_table = False
+        for line in fh:
+
+            if start_text in line:
+                in_table = True
+                if not include_start:
+                    continue
+
+            elif end_text in line:
+                if include_end:
+                    yield line
+                break
+
+            if in_table:
+                yield line
 
 
 if __name__ == '__main__':
@@ -25,10 +46,70 @@ def extract_data_lines(filename, start_text, end_text, include_start=False, incl
     # This will involve a for loop that iterates over `data_lines`
     # For each row, you will append a tuple to `records`
 
+    # '<tr><td align="center">2017</td>\n'
+    # '<td>Emma</td> <td>Olivia</td> <td>Ava</td> <td>Isabella</td> <td>Sophia</td> <td>Liam</td> <td>Noah</td> <td>William</td> <td>James</td> <td>Logan</td></tr>\n'
+
+    for line in data_lines:
+        if 'align="center"' in line:
+            year = int(line.strip().split('<tr><td align="center">')[1].split('</td>')[0])
+        else:
+            names = [it.split('</td>')[0] for it in line.strip().split('<td>')[1:]]
+            for ii, name in enumerate(names):
+                rank = (ii % 5) + 1
+                gender = 'female' if ii < 5 else 'male'
+                records.append((year, gender, rank, name))
+
     data = pd.DataFrame.from_records(records, columns=['year', 'gender', 'rank', 'name'])
 
     # Add your code for Step 3 here
     # You will use `data` to find and print out the answers for each questions listed in Task 4
 
     # For example, to answer question 1:
+    # 1. Which years Emma is the most chosen names?
     print(data.query('name == "Emma"').query('rank == 1')['year'].tolist())
+    print(data.query('name == "Emma" and rank == 1')['year'].tolist())
+
+    # 2. Which name had been the most chosen name for the longest consecutive years?
+    male = {'previous': '', 'cnt': 0, 'runs': []}
+    female = {'previous': '', 'cnt': 0, 'runs': []}
+    for __, it in data.query('rank == 1').iterrows():
+        dd = male if it.gender == 'male' else female
+        if it['name'] == dd['previous']:
+            dd['cnt'] += 1
+        else:
+            if dd['previous'] != '':
+                dd['runs'].append((dd['previous'], dd['cnt']))
+            dd['cnt'] = 1
+            dd['previous'] = it['name']
+
+    runs = pd.DataFrame(male['runs'] + female['runs'], columns=['name', 'cnt'])
+    print('Name with the longest run at the top:', runs.name[runs.cnt.idxmax()])
+
+    # 3. How many unique male names have be on top 5 between years 1980 and 2000?
+    print(len(set(data.query('year >= 1980 and year <= 2000 and gender == "male"')['name'])))
+
+    # 4. Are there more unique male names or more unique female names that are on top 5?
+    unique_male_names = len(set(data.query('gender == "male"')['name']))
+    unique_female_names = len(set(data.query('gender == "female"')['name']))
+
+    if unique_male_names > unique_female_names:
+        print('More unique male names.')
+    elif unique_male_names < unique_female_names:
+        print('More unique female names.')
+    else:
+        print('Same number of unique male and female names.')
+
+    # 5. What is the distribution of the numbers of consecutive years that a male name remains the most chosen name?
+    previous = ''
+    count = 0
+    runs = []
+    for it in data.query('gender == "male" and rank == 1')['name']:
+        if it == previous:
+            count += 1
+        else:
+            if previous != '':
+                runs.append(count)
+            count = 1
+            previous = it
+
+    print('Distribution:\n', pd.value_counts(runs))