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Pandas DataFrame UltraQuick Tutorial

This Colab introduces DataFrames, which are the central data structure in the pandas API. This Colab is not a comprehensive DataFrames tutorial. Rather, this Colab provides a very quick introduction to the parts of DataFrames required to do the other Colab exercises in Machine Learning Crash Course.

A DataFrame is similar to an in-memory spreadsheet. Like a spreadsheet:

A DataFrame stores data in cells.
A DataFrame has named columns (usually) and numbered rows.

Import NumPy and pandas modules

Run the following code cell to import the NumPy and pandas modules.



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import numpy as np
import pandas as pd

Creating a DataFrame

The following code cell creates a simple DataFrame containing 10 cells organized as follows:

5 rows
2 columns, one named temperature and the other named activity

The following code cell instantiates a pd.DataFrame class to generate a DataFrame. The class takes two arguments:

The first argument provides the data to populate the 10 cells. The code cell calls np.array to generate the 5x2 NumPy array.
The second argument identifies the names of the two columns.



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# Create and populate a 5x2 NumPy array.
my_data = np.array([[0, 3], [10, 7], [20, 9], [30, 14], [40, 15]])

# Create a Python list that holds the names of the two columns.
my_column_names = ['temperature', 'activity']

# Create a DataFrame.
my_dataframe = pd.DataFrame(data=my_data, columns=my_column_names)

# Print the entire DataFrame
print(my_dataframe)

Adding a new column to a DataFrame

You may add a new column to an existing pandas DataFrame just by assigning values to a new column name. For example, the following code creates a third column named adjusted in my_dataframe:



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# Create a new column named adjusted.
my_dataframe["adjusted"] = my_dataframe["activity"] + 2

# Print the entire DataFrame
print(my_dataframe)

Specifying a subset of a DataFrame

Pandas provide multiples ways to isolate specific rows, columns, slices or cells in a DataFrame.



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print("Rows #0, #1, and #2:")
print(my_dataframe.head(3), '\n')

print("Row #2:")
print(my_dataframe.iloc[[2]], '\n')

print("Rows #1, #2, and #3:")
print(my_dataframe[1:4], '\n')

print("Column 'temperature':")
print(my_dataframe['temperature'])

Task 1: Create a DataFrame

Do the following:

Create an 3x4 (3 rows x 4 columns) pandas DataFrame in which the columns are named Eleanor, Chidi, Tahani, and Jason. Populate each of the 12 cells in the DataFrame with a random integer between 0 and 100, inclusive.
Output the following:
- the entire DataFrame
- the value in the cell of row #1 of the Eleanor column
Create a fifth column named Janet, which is populated with the row-by-row sums of Tahani and Jason.

To complete this task, it helps to know the NumPy basics covered in the NumPy UltraQuick Tutorial.



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# Write your code here.



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#@title Double-click for a solution to Task 1.

# Create a Python list that holds the names of the four columns.
my_column_names = ['Eleanor', 'Chidi', 'Tahani', 'Jason']

# Create a 3x4 numpy array, each cell populated with a random integer.
my_data = np.random.randint(low=0, high=101, size=(3, 4))

# Create a DataFrame.
df = pd.DataFrame(data=my_data, columns=my_column_names)

# Print the entire DataFrame
print(df)

# Print the value in row #1 of the Eleanor column.
print("\nSecond row of the Eleanor column: %d\n" % df['Eleanor'][1])

# Create a column named Janet whose contents are the sum
# of two other columns.
df['Janet'] = df['Tahani'] + df['Jason']

# Print the enhanced DataFrame
print(df)

Copying a DataFrame (optional)

Pandas provides two different ways to duplicate a DataFrame:

Referencing. If you assign a DataFrame to a new variable, any change to the DataFrame or to the new variable will be reflected in the other.
Copying. If you call the pd.DataFrame.copy method, you create a true independent copy. Changes to the original DataFrame or to the copy will not be reflected in the other.

The difference is subtle, but important.



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# Create a reference by assigning my_dataframe to a new variable.
print("Experiment with a reference:")
reference_to_df = df

# Print the starting value of a particular cell.
print("  Starting value of df: %d" % df['Jason'][1])
print("  Starting value of reference_to_df: %d\n" % reference_to_df['Jason'][1])

# Modify a cell in df.
df.at[1, 'Jason'] = df['Jason'][1] + 5
print("  Updated df: %d" % df['Jason'][1])
print("  Updated reference_to_df: %d\n\n" % reference_to_df['Jason'][1])

# Create a true copy of my_dataframe
print("Experiment with a true copy:")
copy_of_my_dataframe = my_dataframe.copy()

# Print the starting value of a particular cell.
print("  Starting value of my_dataframe: %d" % my_dataframe['activity'][1])
print("  Starting value of copy_of_my_dataframe: %d\n" % copy_of_my_dataframe['activity'][1])

# Modify a cell in df.
my_dataframe.at[1, 'activity'] = my_dataframe['activity'][1] + 3
print("  Updated my_dataframe: %d" % my_dataframe['activity'][1])
print("  copy_of_my_dataframe does not get updated: %d" % copy_of_my_dataframe['activity'][1])