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# ---
# DataCamp example
# ---

"""
Finding files that match a pattern

the glob module has a function called glob that takes a pattern and returns a list of the files in the working directory that match that pattern.

For example, if you know the pattern is part_ single digit number .csv, you can write the pattern as 'part_?.csv' (which would match part_1.csv, part_2.csv, part_3.csv, etc.)

Similarly, you can find all .csv files with '*.csv', or all parts with 'part_*'. The ? wildcard represents any 1 character, and the * wildcard represents any number of characters.
"""
# Import necessary modules
import pandas as pd
import glob

# Write the pattern: pattern
pattern = '*.csv'

# Save all file matches: csv_files
csv_files = glob.glob(pattern)

# Print the file names
print(csv_files)

# Load the second file into a DataFrame: csv2
csv2 = pd.read_csv(csv_files[1])

# Print the head of csv2
print(csv2.head())

# ---
# example 2
# ---
"""
You'll start with an empty list called frames. Your job is to use a for loop to:

    iterate through each of the filenames
    read each filename into a DataFrame, and then
    append it to the frames list.

You can then concatenate this list of DataFrames using pd.concat()
"""
# Create an empty list: frames
frames = []

#  Iterate over csv_files
for csv in csv_files:

    #  Read csv into a DataFrame: df
    df = pd.read_csv(csv)
    
    # Append df to frames
    frames.append(df)

# Concatenate frames into a single DataFrame: uber
uber = pd.concat(frames)