Python Data Science Toolbox 3 - lambda function

- 람다 함수 잘 모르겠어서 이 부분 더 찾아보기

 

- 연습

# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'pippin', 'aragorn', 'boromir', 'legolas', 'gimli', 'gandalf']
# Use filter() to apply a lambda function over fellowship: result
result = filter(lambda member: len(member) > 6, fellowship)
# Convert result to a list: result_list
result_list = list(result)
# Print result_list
print(result_list)

 

- 연습 2

# Import reduce from functools
from functools import reduce
# Create a list of strings: stark
stark = ['robb', 'sansa', 'arya', 'brandon', 'rickon']
# Use reduce() to apply a lambda function over stark: result
result = reduce(lambda item1, item2: item1 + item2, stark)
# Print the result
print(result)

 

- 연습 3

# Define shout_echo
def shout_echo(word1, echo=1):
    """Concatenate echo copies of word1 and three
    exclamation marks at the end of the string."""

    # Raise an error with raise
    if echo < 0:
        raise ValueError('echo must be greater than or equal to 0')

    # Concatenate echo copies of word1 using *: echo_word
    echo_word = word1 * echo

    # Concatenate '!!!' to echo_word: shout_word
    shout_word = echo_word + '!!!'

    # Return shout_word
    return shout_word

# Call shout_echo
shout_echo("particle", echo=5)

 

- 연습 4

# Select retweets from the Twitter DataFrame: result
result = filter(lambda x: x[0:2] == 'RT', tweets_df['text'])

# Create list from filter object result: res_list
res_list = list(result)

# Print all retweets in res_list
for tweet in res_list:
    print(tweet)

 

- 연습 5

# Define count_entries()
def count_entries(df, col_name='lang'):
    """Return a dictionary with counts of
    occurrences as value for each key."""

    # Initialize an empty dictionary: cols_count
    cols_count = {}

    # Add try block
    try: 
        # Extract column from DataFrame: col
        col = df[col_name]
        
        # Iterate over the column in DataFrame
        for entry in col:
    
            # If entry is in cols_count, add 1
            if entry in cols_count.keys():
                cols_count[entry] += 1
            # Else add the entry to cols_count, set the value to 1
            else:
                cols_count[entry] = 1
    
        # Return the cols_count dictionary
        return cols_count

    # Add except block
    except:
        print('The DataFrame does not have a ' + col_name + ' column.')

# Call count_entries(): result1
result1 = count_entries(tweets_df, 'lang')

# Print result1
print(result1)

 

- 연습 5

# Define count_entries()
def count_entries(df, col_name='lang'):
    """Return a dictionary with counts of
    occurrences as value for each key."""
    
    # Raise a ValueError if col_name is NOT in DataFrame
    if col_name not in df.columns:
        raise ValueError('The DataFrame does not have a ' + col_name + ' column.')

    # Initialize an empty dictionary: cols_count
    cols_count = {}
    
    # Extract column from DataFrame: col
    col = df[col_name]
    
    # Iterate over the column in DataFrame
    for entry in col:

        # If entry is in cols_count, add 1
        if entry in cols_count.keys():
            cols_count[entry] += 1
            # Else add the entry to cols_count, set the value to 1
        else:
            cols_count[entry] = 1
        
        # Return the cols_count dictionary
    return cols_count

# Call count_entries(): result1
result1 = count_entries(tweets_df, 'lang')

# Print result1
print(result1)