Task 11

task-11/get_top_names.py

@@ -0,0 +1,128 @@
+"""
+get_top_names.py
+For astrophg/learning-by-doing: Task 9
+https://github.com/astropgh/learning-by-doing/tree/master/task-09
+"""
+
+import pandas as pd
+import sqlite3
+
+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`.
+    """
+    started = False
+    with open(filename) as fh:
+        for line in fh:
+            if started:
+                if end_text in line:
+                    if include_end:
+                        yield line
+                    break
+                yield line
+            elif start_text in line:
+                started = True
+                if include_start:
+                    yield line
+
+
+class NameRecorder:
+    def __init__(self):
+        self.records = []
+        self.year = None
+
+
+    def add(self, name, is_female, rank):
+
+
+        if self.year is None:
+
+            raise ValueError('One must set year first')
+
+
+        if is_female:
+
+            self.gender = 'Female'
+
+        else:
+
+            self.gender = 'Male'
+
+
+        self.records.append((self.year, self.gender, rank, name))
+
+
+    def to_pandas(self):
+
+        if self.records == []:
+
+            raise ValueError('Empty data base')
+
+        return pd.DataFrame.from_records(self.records, columns=['year', 'gender', 'rank', 'name'])
+
+
+    def to_sql(self, filename = None):
+
+        # https://www.dataquest.io/blog/python-pandas-databases/
+        # Check output by typing:
+        # recorder.to_sql("names")
+        # import sqlite3
+        # conn = sqlite3.connect("names.db")
+        # pd.read_sql_query("select * from names;", conn)
+
+        if filename is None:
+
+            raise ValueError('Please, insert file name')
+
+        # From pandas to SQL
+
+        conn = sqlite3.connect("%s.db" % (filename))
+        df = recorder.to_pandas()
+        return df.to_sql(filename, conn, if_exists="replace")
+
+
+    def clear(self):
+        self.records.clear()
+        self.year = None
+
+
+if __name__ == '__main__':
+    filename = '../task-03/top5names.html'
+    start_text = '<tr><td align="center">2017</td>'
+    end_text = '</table></center></div><!-- end #content -->'
+
+    data_lines = extract_data_lines(filename, start_text, end_text, include_start=True)
+
+    recorder = NameRecorder()
+
+for i, data_line in enumerate(data_lines):
+
+    if i % 2:
+
+        names = data_line.replace('<td>', ' ').replace('</td>', ' ').replace('</tr>', ' ').strip().split()
+
+        if len(names) == 10:
+
+            for j, name in enumerate(names):
+
+                recorder.add(name, is_female=(j < 5), rank=(j % 5 + 1))
+
+        else:
+
+            raise RuntimeError('There should be ten names per row. Take a look at your file, line:', data_line)
+
+    else:
+
+        try:
+
+            recorder.year = int(data_line.replace('<tr><td align="center">', '').strip()[:4])
+
+        except (ValueError):
+
+            raise RuntimeError('The text formatting is different than what recorder assumes. Take a look at your file, line:', data_line)
+
+data = recorder.to_pandas()
+
+print(data.query('name == "Emma"').query('rank == 1')['year'].tolist())