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Writing Efficient Python Code
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    • Writing Efficient Python Code

    Run the hidden code cell below to import the data used in this course.

    # Importing pandas
import pandas as pd

# Reading in the data
giants_df = pd.read_csv("datasets/baseball.csv")

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Print the list created using the Non-Pythonic approach
names = ['Jerry', 'Kramer', 'Elaine', 'George', 'Newman']
i = 0
new_list= []
while i < len(names):
    if len(names[i]) >= 6:
        new_list.append(names[i])
    i += 1
print(names)
print(new_list)
    # Print the list created by looping over the contents of names
names = ['Jerry', 'Kramer', 'Elaine', 'George', 'Newman']
better_list = []
for name in names:
    if len(name) >= 6:
        better_list.append(name)
print(better_list)
    # Print the list created by using list comprehension
names = ['Jerry', 'Kramer', 'Elaine', 'George', 'Newman']
best_list = [noname for noname in names if len(noname) >= 6]
print(best_list)
    import this
print(type(this))
    # Create a range object that goes from 0 to 5
nums = range(0, 6)
print(type(nums))

# Convert nums to a list
nums_list = list(nums)
print(nums_list)

# Create a new list of odd numbers from 1 to 11 by unpacking a range object
nums_list2 = [*range(1,12,2)]
print(nums_list2)
    """
enumerate() allows to create an index for each item in the object. Use list comprehension, or even unpack the _enumerate object_ directly into a list, to write a nice simple one-liner.
"""

# Rewrite the for loop to use enumerate
indexed_names = []
for i, name in enumerate(names):
    index_name = (i,name)
    indexed_names.append(index_name) 
print(indexed_names)

# Rewrite the above for loop using list comprehension
indexed_names_comp = [(i, name) for i,name in enumerate(names)]
print(indexed_names_comp)

# Unpack an enumerate object with a starting index of one
indexed_names_unpack = [*enumerate(names, 1)]
print(indexed_names_unpack)
    # Use map to apply str.upper to each element in names
names_map  = map(str.upper, names)

# Print the type of the names_map
print(type(names_map))

# Unpack names_map into a list
names_uppercase = [list(names_map)]

# Print the list created above
print(names_uppercase)
    import numpy as np

nni = np.array([1, 2, 3])
print(nni.dtype)
nnf = np.array([1, 2.5, 3])
print(nnf.dtype)
    1) Print the second row of nums.
    2) Print the items of nums that are greater than six.
    3) Create nums_dbl that doubles each number in nums.
    4) Replace the third column in nums with a new column that adds 1 to each item in the original column.
    Hints:

    numpy arrays are zero indexed.

    That means [0,:] will get the first row of a 2D array.

    You can create a boolean index by using an inequality directly on the nums array. For example, nums < 2 will create a boolean index for all items in nums that are less than two.

    Mathematical operations are broadcasted to each element of a numpy array by default. (i.e., the command nums + 1 will add one to each element in nums)

    import numpy as np
nums = [[1,2,3,4,5],
        [5,4,3,2,1]]

nums_arr = np.array(nums)
    # Print second row of nums
print(nums_arr[1,:])

# Print all elements of nums that are greater than six
print(nums_arr[nums_arr > 6])

# Double every element of nums
nums_dbl = nums_arr * 2
print(nums_dbl)

# Replace the third column of nums
nums_arr[:,2] = nums_arr[:,2] + 1.12
print(nums)
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