# Python Data Analysis Basics


ages = []

for row in moma:
    date = row[6]
    birth = row[3]
    if type(birth) == int:
        age = date - birth
    else:
        age = 0
    ages.append(age)
    
final_ages = []
for a in ages:
    if a > 20:
        final_age = a
    else:
        final_age = "Unknown"
    final_ages.append(final_age)

In order to do this, we can take advantage of the fact that, behind the scenes, Python stores strings in a list-like structure, which lets us slice them in the same way we would a list.

Let's look at a simple example:


In order to use this technique with our ages, we'll need to:

Convert the integer value to a string.
Use slicing to slice all but the last character.


> Create an empty list, decades, to store the artist decade data.
> Iterate over the values in ages, and in each iteration:
> If age is "Unknown", assign it to the variable decade
> If age isn't "Unknown":
> Convert the integer value to a string, assigning it to the variable decade.
> Use list slicing to remove the final character of decade.
> Use the + operator to add the substring "0s" to the end of the string decade.
> Append decade to the decades list.

decades = []
for age in ages:
    if age == "Unknown":
        decade = age
    else:
        decade = str(age)
        decade = decade[:-1]
        decade = decade + "0s"
    decades.append(decade)

A frequency table shows us how many of each item we have on our list:
The logical steps we'll need to follow are shown in the diagram below:

decade_frequency = {}

for d in decades:
    if d not in decade_frequency:
        decade_frequency[d] = 1
    else:
        decade_frequency[d] += 1

#### [The str.format() method is a powerful tool that helps us write easy-to-read code while combining strings with other variables.](https://docs.python.org/3/library/stdtypes.html#str.format )

artist = "Pablo Picasso"
birth_year = 1881

template = "{name}'s birth year is {year}"
output = template.format(name=artist, year=birth_year)
print(output)

The code block below might look a bit long and intimidating at first glance, but everything in it is a concept you've seen before. We've added comments so you can follow along with the logic.



def artist_summary(artist):
    num_artworks = artist_freq[artist]
    template = "There are {num} artworks by {name} in the data set"
    output = template.format(name=artist, num=num_artworks)
    print(output)
    
artist_summary("Henri Matisse")


The output of this code is below:
Your bank balance is $12,345.68

pop_millions = [
    ["China", 1379.302771],
    ["India", 1281.935991],
    ["USA",  326.625791],
    ["Indonesia",  260.580739],
    ["Brazil",  207.353391],
]

template = "The population of {} is {:,.2f} million"

for country in pop_millions:
    name = country[0]
    pop = country[1]
    output = template.format(name, pop)
    print(output)

We use the [dict.items() method](https://docs.python.org/3/tutorial/datastructures.html#looping-techniques) which returns each of the key-value pairs from our dictionary one-at-a-time. This helps us loop over dictionaries more easily. We can assign both the key and value (in that order) when we define our loop:

gender_freq = {}

for row in moma:
    gender = row[5]
    if gender not in gender_freq:
        gender_freq[gender] = 1
    else:
        gender_freq[gender] += 1

for gender, num in gender_freq.items():
    template = "There are {n:,} artworks by {g} artists"
    print(template.format(g=gender, n=num))

[Python Documentation: Format Specifications](https://docs.python.org/3/library/string.html#formatspec)
[PyFormat: Python String Formatting Reference](https://pyformat.info/)

###### tags: `python`