This Pandas Tutorial lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
Pandas is the usual tool for tables of rows and columns. A DataFrame is that table, and a Series is one column.
The runner here has no Pandas. A list of dicts shows the same filter you would write with a column comparison.
Example 1: Start here
Locally, read a sheet with pandas.read_csv and filter with df[df["score"] >= 80].
rows = [{"name": "Meera", "score": 82}, {"name": "Arun", "score": 74}] # a list; the values stay in this order
print([row["name"] for row in rows if row["score"] >= 80]) # keep the items that pass the test
Example 2: Read one column
Pandas Tutorial treats a table as rows. This prints only the name column.
rows = [{"name": "Meera", "score": 36}, {"name": "Arun", "score": 74}] # a list; the values stay in this order
print([row["name"] for row in rows]) # in is True when the value is found
Example 3: Filter the table
A filter keeps the rows that pass a test. Meera stays when her score is at least 80.
rows = [{"name": "Meera", "score": 36}, {"name": "Arun", "score": 74}] # a list; the values stay in this order
print([row["name"] for row in rows if row["score"] >= 80]) # keep the items that pass the test
Example 4: Add a column
A new column is just a new key. passed is True when the score is high enough.
row = {"name": "Meera", "score": 36} # a dictionary; each key has one value
row["passed"] = row["score"] >= 80 # change this one field
print(row)
Locally, read a sheet with pandas.read_csv and filter with df[df["score"] >= 80].
