Open the python_script_student.py file in a text editor, copy the contents to a notebook in IPython, and execute the operations.

Copy and paste the code from the Python script into a Jupyter notebook:

import numpy as np

def square_plus(x, c):
    return np.power(x, 2) + c

Now, update the values of the x and c variables. Then, change the definition of the function:

x = 10
c = 100

result = square_plus(x, c)
print(result)

The output is as follows:

200

Import pandas and NumPy library:

import pandas as pd
import numpy as np

Read the RadNet dataset from the U.S. Environmental Protection Agency, available from the Socrata project:

url = "https://opendata.socrata.com/api/views/cf4r-dfwe/rows.csv?accessType=DOWNLOAD"
df = pd.read_csv(url)

Create a list with numeric columns for radionuclides in the RadNet dataset:

columns = df.columns
id_cols = ['State', 'Location', "Date Posted", 'Date Collected', 'Sample Type', 'Unit']
columns = list(set(columns) - set(id_cols))
columns

Use the apply method on one column, with a lambda function that compares the Non-detect string:

df['Cs-134'] = df['Cs-134'].apply(lambda x: np.nan if x == "Non-detect" else x)
df.head()

The output is as follows:

Figure 1.19: DataFrame after applying the lambda function

Replace the text values with NaN in one column with np.nan:

df.loc[:, columns] = df.loc[:, columns].applymap(lambda x: np.nan if x == 'Non-detect' else x)
df.loc[:, columns] = df.loc[:, columns].applymap(lambda x: np.nan if x == 'ND' else x)

Use the same lambda comparison and use the applymap method on several columns at the same time, using the list created in the first step:

df.loc[:, ['State', 'Location', 'Sample Type', 'Unit']] = df.loc[:, ['State', 'Location',g 'Sample Type', 'Unit']].applymap(lambda x: x.strip())

Create a list of the remaining columns that are not numeric:

df.dtypes

The output is as follows:

Figure 1.20: List of columns and their type

Convert the DataFrame objects into floats using the to_numeric function:

df['Date Posted'] = pd.to_datetime(df['Date Posted'])
df['Date Collected'] = pd.to_datetime(df['Date Collected'])
for col in columns:
    df[col] = pd.to_numeric(df[col])
df.dtypes

The output is as follows:

Figure 1.21: List of columns and their type

Using the selection and filtering methods, verify that the names of the string columns don't have any spaces:

df['Date Posted'] = pd.to_datetime(df['Date Posted'])
df['Date Collected'] = pd.to_datetime(df['Date Collected'])
for col in columns:
    df[col] = pd.to_numeric(df[col])
df.dtypes

The output is as follows:

Figure 1.22: DataFrame after applying the selection and filtering method

Use the RadNet DataFrame that we have been working with.

Fix all the data type problems, as we saw before.

Create a plot with a filter per Location, selecting the city of San Bernardino, and one radionuclide, with the x-axis set to the date and the y-axis with radionuclide I-131:

df.loc[df.Location == 'San Bernardino'].plot(x='Date Collected', y='I-131')

The output is as follows:

Figure 1.23: Plot of Date collected vs I-131

Create a scatter plot with the concentration of two related radionuclides, I-131 and I-132:

fig, ax = plt.subplots()
ax.scatter(x=df['I-131'], y=df['I-132'])
_ = ax.set(
    xlabel='I-131',
    ylabel='I-132',
    title='Comparison between concentrations of I-131 and I-132'
)

The output is as follows:

Figure 1.24: Plot of concentration of I-131 and I-132