Module 3 Assignment¶

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import csv
from nose.tools import assert_equal, assert_almost_equal
import pandas as pd

Problem 1: Reading files with the CSV Library¶

In this problem you will finish writing a function called read_data that reads the iris dataset. The columns are in this order:

Column 1: sepal length in cm
Column 2: sepal width in cm
Column 3: petal length in cm
Column 4: petal width in cm
Column 5: class

There are multiple ways to solve this problem but ultimately this function should:

Accept a string that will be the path to the iris dataset (we named this parameter file_path)
Store each column of the dataset in different list.
Convert Columns 1-4 to floats.
Return each of the columns (total of 5) in the following order:
- sepal length in cm
- petal length in cm
- sepal width in cm
- petal width in cm
- class

def read_data(file_path):
    '''
    This function reads data from the iris data and returns 5 lists (1 for each column)
    
    file_path: string that specifies path to iris dataset
    '''
    
    ### YOUR CODE HERE

file_path = './iris.csv'
sepal_len, petal_len, sepal_wid, petal_wid, class_ = read_data(file_path)

assert_equal(sepal_len[0:50], [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4,
                               4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1,
                               4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2,
                               5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0,
                               5.1, 4.8, 5.1, 4.6, 5.3, 5.0])
assert_equal(sepal_len[75:100], [6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0,
                                 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7,
                                 6.2, 5.1, 5.7])

assert_equal(petal_len[0:25], [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5,
                               1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5,
                               1.0, 1.7, 1.9])
assert_equal(petal_len[44:67], [1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6,
                                4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5])

assert_equal(sepal_wid[0:33], [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7,
                               3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7,
                               3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1])
assert_equal(sepal_wid[44:100], [3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8,
                                 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1,
                                 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8,
                                 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3,
                                 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8])

assert_equal(petal_wid[15:40], [0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4,
                                0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.1, 0.2, 0.2, 0.1, 0.2,
                                0.2])
assert_equal(petal_wid[45:78], [0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6,
                                1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5,
                                1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7])

assert_equal(class_[45:55], ['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                             'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',
                             'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor'])
assert_equal(class_[75:90], ['Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
                             'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
                             'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
                             'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
                             'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor'])

Problem 2: Finding column names¶

Recall that the column names of the data frame, df, can all be found at once using "df.columns".

In this problem you will use df.columns to find the name of the first two columns of df, and save it in a variable named "firsttwo_column_names".

Hint: Treat df.columns like an array or a list.

df = pd.read_csv('./iris.csv')
### YOUR CODE HERE

assert_equal(type(firsttwo_column_names), pd.indexes.base.Index)
assert_equal(len(firsttwo_column_names), 2)

Problem 3: Writing to a file¶

Now that we read data from the iris dataset we will write a subset of it to another file. The following function write_file takes in a file_path to write to, all 5 columns from the iris dataset and the amount of lines to write to the new file.

For this problem do the following:

Up until the amount specified , write each column to file_path in the following order: sepal length in cm , petal length in cm, sepal width in cm, petal width in cm, class.
Each of item exclduing the last column should be delimited with only a comma (no spaces). An example row should look like the following:
- 5.7,2.5,5.0,2.0,Iris-virginica

def write_file(file_path, sepal_len, petal_len, sepal_wid, petal_wid, class_, amount=25):
    '''
    This function writes up to `amount` rows using the 5 columns of the iris data 
    to file named file_path
    
    file_path : string path to write iris dataset to
    sepal_len : sepal length in cm column in iris dataset
    petal_len : petal length in cm column in iris dataset
    sepal_wid : sepal width in cm column in iris dataset
    petal_wid : petal width in cm column in iris dataset
    
    class_ : class column in iris dataset
    
    amount: Amount of rows to write to file_path
    '''
    
    ### YOUR CODE HERE

iris25rows_path = './iris25.csv'
write_file(iris25rows_path, sepal_len, petal_len, sepal_wid, petal_wid, class_, amount=25)
column3 = []
column5 = []
with open(iris25rows_path, 'r') as f:
    for row in csv.reader(f):
        column3.append(row[2])
        column5.append(row[4])

assert_equal(column3, ['3.5', '3.0', '3.2', '3.1', '3.6', '3.9', '3.4', '3.4',
                           '2.9', '3.1', '3.7', '3.4', '3.0', '3.0', '4.0', '4.4',
                           '3.9', '3.5', '3.8', '3.8', '3.4', '3.7', '3.6', '3.3', '3.4'])

assert_equal(column5, ['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
                       'Iris-setosa'])

iris99rows_path = './iris99.csv'
write_file(iris99rows_path, sepal_len, petal_len, sepal_wid, petal_wid, class_, amount=100)

data = []
with open(iris99rows_path, 'r') as f:
    for row in csv.reader(f):
        data.append(row)

assert_equal(data, [['5.1', '1.4', '3.5', '0.2', 'Iris-setosa'], ['4.9', '1.4', '3.0', '0.2', 'Iris-setosa'], ['4.7', '1.3', '3.2', '0.2', 'Iris-setosa'], ['4.6', '1.5', '3.1', '0.2', 'Iris-setosa'], ['5.0', '1.4', '3.6', '0.2', 'Iris-setosa'], ['5.4', '1.7', '3.9', '0.4', 'Iris-setosa'], ['4.6', '1.4', '3.4', '0.3', 'Iris-setosa'], ['5.0', '1.5', '3.4', '0.2', 'Iris-setosa'], ['4.4', '1.4', '2.9', '0.2', 'Iris-setosa'], ['4.9', '1.5', '3.1', '0.1', 'Iris-setosa'], ['5.4', '1.5', '3.7', '0.2', 'Iris-setosa'], ['4.8', '1.6', '3.4', '0.2', 'Iris-setosa'], ['4.8', '1.4', '3.0', '0.1', 'Iris-setosa'], ['4.3', '1.1', '3.0', '0.1', 'Iris-setosa'], ['5.8', '1.2', '4.0', '0.2', 'Iris-setosa'], ['5.7', '1.5', '4.4', '0.4', 'Iris-setosa'], ['5.4', '1.3', '3.9', '0.4', 'Iris-setosa'], ['5.1', '1.4', '3.5', '0.3', 'Iris-setosa'], ['5.7', '1.7', '3.8', '0.3', 'Iris-setosa'], ['5.1', '1.5', '3.8', '0.3', 'Iris-setosa'], ['5.4', '1.7', '3.4', '0.2', 'Iris-setosa'], ['5.1', '1.5', '3.7', '0.4', 'Iris-setosa'], ['4.6', '1.0', '3.6', '0.2', 'Iris-setosa'], ['5.1', '1.7', '3.3', '0.5', 'Iris-setosa'], ['4.8', '1.9', '3.4', '0.2', 'Iris-setosa'], ['5.0', '1.6', '3.0', '0.2', 'Iris-setosa'], ['5.0', '1.6', '3.4', '0.4', 'Iris-setosa'], ['5.2', '1.5', '3.5', '0.2', 'Iris-setosa'], ['5.2', '1.4', '3.4', '0.2', 'Iris-setosa'], ['4.7', '1.6', '3.2', '0.2', 'Iris-setosa'], ['4.8', '1.6', '3.1', '0.2', 'Iris-setosa'], ['5.4', '1.5', '3.4', '0.4', 'Iris-setosa'], ['5.2', '1.5', '4.1', '0.1', 'Iris-setosa'], ['5.5', '1.4', '4.2', '0.2', 'Iris-setosa'], ['4.9', '1.5', '3.1', '0.1', 'Iris-setosa'], ['5.0', '1.2', '3.2', '0.2', 'Iris-setosa'], ['5.5', '1.3', '3.5', '0.2', 'Iris-setosa'], ['4.9', '1.5', '3.1', '0.1', 'Iris-setosa'], ['4.4', '1.3', '3.0', '0.2', 'Iris-setosa'], ['5.1', '1.5', '3.4', '0.2', 'Iris-setosa'], ['5.0', '1.3', '3.5', '0.3', 'Iris-setosa'], ['4.5', '1.3', '2.3', '0.3', 'Iris-setosa'], ['4.4', '1.3', '3.2', '0.2', 'Iris-setosa'], ['5.0', '1.6', '3.5', '0.6', 'Iris-setosa'], ['5.1', '1.9', '3.8', '0.4', 'Iris-setosa'], ['4.8', '1.4', '3.0', '0.3', 'Iris-setosa'], ['5.1', '1.6', '3.8', '0.2', 'Iris-setosa'], ['4.6', '1.4', '3.2', '0.2', 'Iris-setosa'], ['5.3', '1.5', '3.7', '0.2', 'Iris-setosa'], ['5.0', '1.4', '3.3', '0.2', 'Iris-setosa'], ['7.0', '4.7', '3.2', '1.4', 'Iris-versicolor'], ['6.4', '4.5', '3.2', '1.5', 'Iris-versicolor'], ['6.9', '4.9', '3.1', '1.5', 'Iris-versicolor'], ['5.5', '4.0', '2.3', '1.3', 'Iris-versicolor'], ['6.5', '4.6', '2.8', '1.5', 'Iris-versicolor'], ['5.7', '4.5', '2.8', '1.3', 'Iris-versicolor'], ['6.3', '4.7', '3.3', '1.6', 'Iris-versicolor'], ['4.9', '3.3', '2.4', '1.0', 'Iris-versicolor'], ['6.6', '4.6', '2.9', '1.3', 'Iris-versicolor'], ['5.2', '3.9', '2.7', '1.4', 'Iris-versicolor'], ['5.0', '3.5', '2.0', '1.0', 'Iris-versicolor'], ['5.9', '4.2', '3.0', '1.5', 'Iris-versicolor'], ['6.0', '4.0', '2.2', '1.0', 'Iris-versicolor'], ['6.1', '4.7', '2.9', '1.4', 'Iris-versicolor'], ['5.6', '3.6', '2.9', '1.3', 'Iris-versicolor'], ['6.7', '4.4', '3.1', '1.4', 'Iris-versicolor'], ['5.6', '4.5', '3.0', '1.5', 'Iris-versicolor'], ['5.8', '4.1', '2.7', '1.0', 'Iris-versicolor'], ['6.2', '4.5', '2.2', '1.5', 'Iris-versicolor'], ['5.6', '3.9', '2.5', '1.1', 'Iris-versicolor'], ['5.9', '4.8', '3.2', '1.8', 'Iris-versicolor'], ['6.1', '4.0', '2.8', '1.3', 'Iris-versicolor'], ['6.3', '4.9', '2.5', '1.5', 'Iris-versicolor'], ['6.1', '4.7', '2.8', '1.2', 'Iris-versicolor'], ['6.4', '4.3', '2.9', '1.3', 'Iris-versicolor'], ['6.6', '4.4', '3.0', '1.4', 'Iris-versicolor'], ['6.8', '4.8', '2.8', '1.4', 'Iris-versicolor'], ['6.7', '5.0', '3.0', '1.7', 'Iris-versicolor'], ['6.0', '4.5', '2.9', '1.5', 'Iris-versicolor'], ['5.7', '3.5', '2.6', '1.0', 'Iris-versicolor'], ['5.5', '3.8', '2.4', '1.1', 'Iris-versicolor'], ['5.5', '3.7', '2.4', '1.0', 'Iris-versicolor'], ['5.8', '3.9', '2.7', '1.2', 'Iris-versicolor'], ['6.0', '5.1', '2.7', '1.6', 'Iris-versicolor'], ['5.4', '4.5', '3.0', '1.5', 'Iris-versicolor'], ['6.0', '4.5', '3.4', '1.6', 'Iris-versicolor'], ['6.7', '4.7', '3.1', '1.5', 'Iris-versicolor'], ['6.3', '4.4', '2.3', '1.3', 'Iris-versicolor'], ['5.6', '4.1', '3.0', '1.3', 'Iris-versicolor'], ['5.5', '4.0', '2.5', '1.3', 'Iris-versicolor'], ['5.5', '4.4', '2.6', '1.2', 'Iris-versicolor'], ['6.1', '4.6', '3.0', '1.4', 'Iris-versicolor'], ['5.8', '4.0', '2.6', '1.2', 'Iris-versicolor'], ['5.0', '3.3', '2.3', '1.0', 'Iris-versicolor'], ['5.6', '4.2', '2.7', '1.3', 'Iris-versicolor'], ['5.7', '4.2', '3.0', '1.2', 'Iris-versicolor'], ['5.7', '4.2', '2.9', '1.3', 'Iris-versicolor'], ['6.2', '4.3', '2.9', '1.3', 'Iris-versicolor'], ['5.1', '3.0', '2.5', '1.1', 'Iris-versicolor'], ['5.7', '4.1', '2.8', '1.3', 'Iris-versicolor']])

Problem 4: Manipulating the Iris Dataset with Panadas¶

We have read in the iris dataset and stored in a pandas dataframe for you in the below cell, this variable is called df.

Your task is to finish the function df_manipulate. Dataframe df is passed into df_manipulate. Using Dataframe df do the following:

Find the sum of the "sepal length in cm" column. Store this in a variable called sepal_length_sum
Transpose the dataframe and store in a variable called dfT

df = pd.read_csv('./iris.csv') # Read in the iris dataset
df.head() # First 5 rows of iris.csv

def df_manipulate(df):
    '''
    This function will return a transposed dataframe and the sum of the
    'sepal length (in cm)' column.
    
    df: dataframe with data from iris dataset
    '''
    
    ### YOUR CODE HERE
    return sepal_length_sum, dfT

sepal_length_sum, dfT = df_manipulate(df)

assert_equal(dfT.equals(df.T), True)
assert_almost_equal(sepal_length_sum, 876.5, places=5)

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