# First, let us import pandas.
import pandas as pd

# Let us build our first DataFrame.
data = {
    'Name': ['Sam', 'Alex', 'Jordan'],
    'Age': [22, 35, 58],
    'City': ['New York', 'Paris', 'London']
}
df = pd.DataFrame(data)

# Look at the DataFrame.
df

# Checking the data type.
type(df)

# Find out how many rows and columns we have.
df.shape

# Look at just the column names.
df.columns

# Look at the first two rows only.
df.head(2)

# Select a single column.
df['Name']

# Get row number 1 (remember: counting starts at 0).
df.iloc[1]

# Select multiple columns.
df[['Name', 'Age']]

# Filter rows based on a condition.
df[df['Age'] > 30]

# Add a new column with values.
df['Country'] = ['USA', 'France', 'UK']
df

# Change a value in the DataFrame.
df.at[2, 'City'] = 'Berlin'
df

# Remove a column using drop.
df = df.drop('Country', axis=1)
df

# Let us delete a row too.
df = df.drop(1, axis=0)
df

# Describe gives you useful stats for numbers.
df.describe()

# Use info to see column types and missing values.
df.info()

# Save your DataFrame to a CSV file.
df.to_csv('my_table.csv', index=False)

# Read your data back into Python.
df_loaded = pd.read_csv('my_table.csv')
df_loaded

# Work with user input to create a new DataFrame.
names = input('Enter three names, separated by commas: ').split(',')
ages = input('Enter their ages, separated by commas: ').split(',')
cities = input('Enter their cities, separated by commas: ').split(',')
user_df = pd.DataFrame({
    'Name': [n.strip() for n in names],
    'Age': [int(a.strip()) for a in ages],
    'City': [c.strip() for c in cities]
})
user_df
})

bm
## Mini-Project: Favorite Movies Table Part 1

Let us build a table of movies and their ratings.

We will rate a few movies and then sort by score.

# Make a DataFrame of movies and ratings.
movie_data = {
    'Movie': ['Inception', 'Moana', 'Avengers'],
    'Rating': [8.7, 7.6, 8.4]
}
movies_df = pd.DataFrame(movie_data)
movies_df

# Sort the table by the best rating at the top.
movies_df.sort_values('Rating', ascending=False)

# Calculate the average rating.
movies_df['Rating'].mean()

# Add a column for how many times you have watched each movie.
movies_df['Watches'] = [2, 10, 3]
movies_df

# Find movies you watched more than three times.
movies_df[movies_df['Watches'] > 3]

# Bonus: Create a DataFrame from a list of lists.
grades = [
    ['Science', 88],
    ['Math', 95],
    ['English', 79]
]
grades_df = pd.DataFrame(grades, columns=['Subject', 'Score'])
grades_df

# Challenge: Create a table for your weekly chores.
chores = input('Enter chores, separated by commas: ').split(',')
minutes = input('Enter minutes for each, separated by commas: ').split(',')
chores_df = pd.DataFrame({
    'Chore': [c.strip() for c in chores],
    'Minutes': [int(m.strip()) for m in minutes]
})
chores_df
})

bm
## Recap and Next Steps

Congratulationsyou have learned the basics of pandas DataFrames!

You now know how to create tables, select data, edit values, and even load from files.

With practice, these tools will help you understand any dataset.