Adventures in Machine Learning

Mastering Pandas DataFrame: 3 Essential Ways to View and Extract Data

Creating a New Column in

Pandas DataFrame Based on Condition

As data analysts, we are often faced with the challenge of generating additional information from an already existing dataset. Some of the critical variables may not have been recorded from the start but could help us derive more insights from the data.

In such cases, we leverage the power of tools such as

Pandas DataFrame to create new columns based on conditions and rules that we define. In this article, we will explore three examples of conditions and how we can use them in creating new columns in the

Pandas DataFrame.

Example 1: Binary Value

Suppose we have a dataset of employees and their performance, which can have a value as either ‘Good’ or ‘Bad.’ We could generate a new column named ‘points,’ which will record the number of points an employee accumulates per month based on their performance. We will assign them 10 points for being ‘Good’ and 5 points for being ‘Bad’ in this case.

To create this column in Pandas, we will make use of the ‘apply’ function to compute the points. In the code snippet below, we select the ‘performance’ column and assign the ‘points’ column using the lambda function.

“`python

def points(x):

if x == ‘Good’:

return 10

else:

return 5

employees[‘points’] = employees[‘performance’].apply(lambda x: points(x))

“`

This code creates a new column named ‘points,’ which records the total number of points a given employee has based on their performance. This method is ideal when you only need to classify data into two categories.

Example 2: Multiple Values

Sometimes, you may need to generate a new column based on specific conditions that are more than two. Suppose, in the example above, we also want a column which classifies employees as Excellent, Average, or Poor.

We can create a new function that considers the points assigned to each employee, applies a classification function to it and compute the value to assign the employee. “`python

def classify(points):

if points >= 25:

return “Excellent”

elif points >= 15:

return “Average”

else:

return “Poor”

employees[‘classification’] = employees[‘points’].apply(lambda x: classify(x))

“`

This code uses the ‘apply’ function and a lambda function to call the ‘classify’ function, which applies the classification rule based on the points assigned to an employee.

The result is a new column named ‘classification,’ which records the employee’s overall performance classification based on points earned. Example 3: Comparison with Existing Column

We can also create a new column in the

Pandas DataFrame by comparing two or more existing columns.

Suppose we have a data set recording basketball players’ assists and rebounds per game. We could generate a new column named ‘assist_more,’ which records whether a given player has more assists than rebounds.

To do this, we create a function that compares both columns for each player and returns the result as a binary value. “`python

def assist_more(row):

if row[‘assists’] > row[‘rebounds’]:

return 1

else:

return 0

players[‘assist_more’] = players.apply(lambda row: assist_more(row), axis=1)

“`

This code creates a new column named ‘assist_more’ that assigns value 1 if the player has more assists than rebounds, and 0 if the player has more rebounds than assists.

Pandas DataFrame

Pandas DataFrame is a powerful tool that data analysts use to manipulate and explore the data. It is a two-dimensional data structure that consists of rows and columns that we can program using Python.

Pandas allow us to import our data, explore it, manipulate it, and visualize it in different ways.

Importing Data

To start using Pandas, we first need to import our data. We can import our data from CSV or Excel files, SQL databases, or other data sources.

Once we load our data, we create a

Pandas DataFrame object from it. The code snippet below shows how to import a CSV file into

Pandas DataFrame.

“`python

import pandas as pd

data = pd.read_csv(‘datafile.csv’)

df = pd.DataFrame(data)

“`

Creating a DataFrame

Sometimes we don’t have external data sources to import, and we need to generate data ourselves. Pandas provides functions such as ‘DataFrame’ that allow us to create a new DataFrame from scratch.

We start by defining our data as a dictionary, then call the ‘DataFrame’ function to create a new DataFrame. “` python

import pandas as pd

data = {“name”: [“John”, “Peter”, “Paul”],

“age”: [26, 32, 24],

“location”: [“Lagos”, “Abuja”, “London”]}

df = pd.DataFrame(data)

“`

This creates a new

Pandas DataFrame with the columns named ‘name,’ ‘age,’ and ‘location,’ with the respective values in the dictionary.

Conclusion

In conclusion,

Pandas DataFrame is an essential tool that data analysts use to manipulate, explore, and visualize data. We can use the ‘apply’ function, lambda functions, and several other functions in Pandas to generate new columns in a DataFrame based on different conditions.

We can also import data from different sources and create new

Pandas DataFrames from scratch. By utilizing Pandas, we can derive more insights from our data and make better data-driven decisions.

Function for Classification

When creating a new column based on specific conditions in a

Pandas DataFrame, we often need to use functions. A function is a block of code that performs a specific task and can be called by other parts of the code.

In data analysis, functions are particularly useful in generating new columns in a

Pandas DataFrame based on different conditions, such as classification. A classification function is one that takes some input, applies a rule, and returns a categorical output.

In this function, we write an if-else statement and parameterize the values that determine a category. By passing these parameters to our function, we can use it for different data set while retaining the same conditions.

For example, suppose we have a dataset of students and their grades. We want to create a classification column called ‘grade_class’ based on the following rules:

– Grade >= 80: ‘Distinction’

– 80 > Grade >= 60: ‘Merit’

– 60 > Grade >= 40: ‘Pass’

– Grade < 40: 'Fail'

We can use a classification function to generate the ‘grade_class’ column in

Pandas DataFrame.

The code snippet below demonstrates how to write the classification function:

“`python

def grade_classification(grade):

if grade >= 80:

return ‘Distinction’

elif grade >= 60:

return ‘Merit’

elif grade >= 40:

return ‘Pass’

else:

return ‘Fail’

“`

Here, we define a function called ‘grade_classification’ that takes a grade as input and applies the classification rule based on if-else statements. The function returns the appropriate category for each grade.

We could then apply this function using the ‘apply’ method in

Pandas DataFrame to create the ‘grade_class’ column for our dataset. Apply Function in

Pandas DataFrame

The ‘apply’ method is a powerful tool in

Pandas DataFrame that allows users to apply a function to each column or row of data in the DataFrame.

It takes a lambda function, which applies the function to each row or column of data, and returns a new DataFrame or Series as output. The ‘apply’ method can be used to create new columns by applying classification functions to existing columns.

The syntax for using the apply method is as follows:

“`python

result = df.apply(lambda x: function(x), axis=1)

“`

Here, ‘df’ refers to the DataFrame, ‘function(x)’ refers to the function that should be applied to every row (or column) of data in the DataFrame, and ‘axis=1’ is used if the function is applied by row.

Since the apply function is slow in Pandas, we can use the ‘applymap’ method to apply a function to every element of the DataFrame, which is faster and more efficient.

It is useful when we need to convert all the values in a DataFrame to a specific data type. The syntax for using the applymap method is as follows:

“`python

df = df.applymap(lambda x: function(x))

“`

Here, ‘df’ refers to the DataFrame, and ‘function(x)’ refers to the function that should be applied to every element of the DataFrame.

For example, suppose we have a dataset of employees and their salaries. We want to create a new column called ‘salary_increase,’ which shows a 10% increase in salary for every employee.

We can accomplish this using the `apply` method. The code snippet below demonstrates how:

“`python

def salary_increase(salary):

return salary * 1.1

employees[‘salary_increase’] = employees[‘salary’].apply(lambda x: salary_increase(x))

“`

This code creates a new column called ‘salary_increase’ by applying the ‘salary_increase’ function to the ‘salary’ column in the DataFrame.

The lambda function is used to pass the values in the ‘salary’ column as parameters to the ‘salary_increase’ function for computation.

Conclusion

In conclusion, the ‘apply’ method and function classification are essential tools in generating new columns in

Pandas DataFrame. By creating appropriate functions with if-else statements and using the ‘apply’ method or ‘applymap’ method to apply them to the DataFrame, we can classify data based on different conditions and generate new columns that provide further insights.

By mastering these tools, data analysts can manipulate, clean, and analyze their data more effectively, and make better data-driven decisions. View

Pandas DataFrame

Pandas is a powerful data analysis tool that gives data analysts the ability to work with data in a variety of ways.

One of the most important aspects of working with data in Pandas is viewing DataFrame. In this article, we will explore different methods of viewing

Pandas DataFrame, including displaying columns, rows, and summary statistics.

Displaying Columns

One of the most common tasks when working with DataFrame is displaying one or more columns of the dataset. Pandas provides a simple way to display one or more columns of the DataFrame using the square bracket notation.

We can access one or more columns of the dataset by calling the DataFrame object and passing the column name or a list of column names in the square bracket. For example, suppose we have a dataset of employees and their salaries.

“`python

import pandas as pd

# read csv

df = pd.read_csv(’employees.csv’)

# show selected column

print(df[‘Salary’])

“`

This code displays the ‘Salary’ column of the dataset. We can also display multiple columns by creating a list containing multiple columns’ names and passing it to the Dataframe object.

“`python

import pandas as pd

# read csv

df = pd.read_csv(’employees.csv’)

# show multiple columns

print(df[[‘Name’, ‘Age’, ‘Salary’]])

“`

This code displays the ‘Name,’ ‘Age,’ and ‘Salary’ columns of the dataset.

Displaying Rows

Another critical task when working with DataFrames is being able to display rows of data from the dataset. We can display rows based on index values, which represent the row numbers or based on some specific conditions that the data meets.

We can display the first or last n rows of the DataFrame using the head() or tail() method. The head() method returns the top n rows of the DataFrame, while the tail() method returns the bottom n rows of the DataFrame.

The default value for n is 5. “`python

# show first 5 rows

print(df.head())

# show last 5 rows

print(df.tail())

“`

We can also display rows based on index values.

We can use the iloc function to get rows based on the index numbers. This function takes two arguments, the start index and the end index, and can return a slice of the DataFrame values.

“`python

# show rows 1 to 3

print(df.iloc[1:4])

“`

This code displays rows 1 to 3 of the dataset.

Displaying Summary Statistics

Pandas provides a simple way to view summary statistics of a DataFrame using the describe() method. This method generates a summary of the data, including the count, mean, standard deviation, minimum, maximum, and quartiles for each numeric column of the DataFrame.

The describe method is particularly useful when we need to summarize the data and get a quick glimpse of its distribution. “`python

print(df.describe())

“`

This code displays the summary statistics of the DataFrame.

We can also display the summary statistics of a specific column using the describe method. “`python

print(df[‘Salary’].describe())

“`

This code displays the summary statistics for the ‘Salary’ column of the dataset.

Customizing the View

Pandas also provides built-in formatting options that allow customizing the output displayed. For example, we can change the number of decimal places displayed by using the set_option method.

“`python

pd.set_option(‘display.float_format’, lambda x: ‘%.2f’ % x)

“`

This code sets the display format of all float types present in DataFrame to show only two decimal places. Similarly, we can customize text or categorical data displayed by using the set_option method.

“`python

pd.set_option(‘display.max_columns’, None)

“`

This code sets the maximum number of columns Pandas will display to none. This means it will display all columns, regardless of their number.

Conclusion

In conclusion, viewing

Pandas DataFrame is an essential part of data analysis. It is important to be able to view specific columns, rows, and summary statistics of data when working with it.

Pandas provides several methods for viewing DataFrames, including displaying columns and rows, and generating summary statistics. Customizing the output format is also possible using the set_option method available in Pandas.

With these tools, Pandas makes it easy for data analysts to manipulate and analyze data, and make better data-driven decisions. In conclusion, viewing and manipulating data in Pandas is crucial in data analysis.

This article outlined three main ways to view and extract data in

Pandas DataFrame. First, we can display columns by name or a group of columns by creating a list.

Second, we can display rows either by index values or based on specific conditions. Finally, we can display summary statistics of our data by using the ‘.describe()’ method.

Additionally, we can customize the output displayed by setting Pandas options using the set_option method. By mastering these tools, data analysts can better understand their data, generate insights, and make more informed data-driven decisions.

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