Lesson 1: What are pandas used for? Pandas Tips and Tricks

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Introduction

Welcome to the realm of Pandas, where data manipulation meets expertise! Let’s dive into the world of Pandas, a Python library that is transforming the way we manage and analyze data.

Also, check:

• Lesson 1: What are pandas used for? Pandas Tips and Tricks
• Lesson 2: What does pandas DataFrame mean?
• Lesson 3: Charts and Diagrams in Colab with Matplotlib
• Lesson 4: Interactive Forms in Google Colab with Python vs. R
• Lesson 5: Working with Local Files, Google Drive, Google Sheets, and Google Cloud Storage in Google Colab

What is Pandas?

Pandas is not your average library—it’s a game-changer in the field of data science and analysis. Short for “Panel Data,” Pandas offers a powerful toolkit for data manipulation, exploration, and analysis, making it essential for anyone dealing with structured data.

Your Data Swiss Army Knife

Think of Pandas as your go-to Swiss Army knife for all things data-related. Whether you’re loading datasets, cleaning up messy data, performing intricate transformations, or analyzing patterns, Pandas has all the tools you need with its array of functions and methods.

Pandas Key Features

Pandas introduces two main data structures: DataFrame and Series. A DataFrame acts like a supercharged spreadsheet, enabling you to store and manipulate data in a tabular format, while a Series represents a single column of data with built-in indexing.

With Pandas, you have the flexibility to manipulate your data in any way you desire. Whether you need to filter rows, add new columns, or summarize data, Pandas simplifies the process with its user-friendly syntax and robust capabilities.

Why Pandas?

So, why opt for Pandas over other tools? Its seamless integration with Python makes it a top choice for Python enthusiasts. Moreover, its speed and efficiency make it well-suited for handling large datasets effortlessly.

Pandas isn’t just for data scientists—it’s for anyone looking to extract insights from data. From business analysts to researchers to hobbyists, Pandas equips users to derive valuable insights and make well-informed decisions.

What are pandas used for?

So, you might have heard about this thing called Pandas in the world of Python. What’s it all about? Well, imagine you’ve got a bunch of data lying around, maybe in a spreadsheet or a database. Pandas swoops in like a superhero to help you make sense of it all.

Cleaning Up the Mess

One of the coolest things about Pandas is how it can handle messy data. You know, missing values, weird outliers, all that jazz. It’s like having a magical broom that sweeps away the junk and leaves you with nice, tidy data.

Let’s Go Exploring

Once your data is all spick and span, Pandas helps you go on an adventure of exploration. You can peek into your dataset, check out the stats, and even spot trends or patterns hiding in the numbers. It’s like being an explorer in the wild world of data.

Bend it, Twist it, Shape it

Pandas is like Play-Doh for data. You can twist and turn your dataset any which way you like. Merge it with another dataset, reshape it into something new, or group it together for some serious analysis. The possibilities are endless!

Getting Visual

Okay, so Pandas doesn’t do fancy graphics on its own, but it’s best buddies with Matplotlib and Seaborn, which are like the artists of the data world. Together, they can turn your boring numbers into beautiful charts and graphs that tell a story.

In and Out, Shake it All About

Importing data from different sources? Exporting your analysis for others to see? Pandas has your back. It can read and write data in all sorts of formats, from CSV to Excel to JSON. It’s like the Swiss Army knife of data handling.

Bottom Line

Pandas is the go-to tool for anyone wrangling data in Python. Whether you’re a data scientist, analyst, or just a curious soul digging into some numbers, Pandas is your loyal companion on the data journey.

Creating a DataFrame from Lists

Code:

import pandas as pd

last_names = ['Connor', 'Connor', 'Reese']
first_names = ['Sarah', 'John', 'Kyle']
df = pd.DataFrame({
  'first_name': first_names,
  'last_name': last_names,
})
df

Explanation:

• Importing Pandas: We start by importing the pandas library, which is essential for creating and manipulating data structures like DataFrames.
• Creating Lists: We have two lists, last_names and first_names, containing last and first names, respectively.
• Creating DataFrame: Using these lists, we create a DataFrame with two columns: ‘first_name’ and ‘last_name’. The pd.DataFrame function takes a dictionary where the keys are column names and the values are the lists.
• Displaying DataFrame: Simply typing df at the end outputs the DataFrame.

Renaming Columns

Code:

import pandas as pd

df = pd.DataFrame({
    'Year': [2016, 2015, 2014, 2013, 2012],
    'Top Animal': ['Giant panda', 'Chicken', 'Pig', 'Turkey', 'Dog']
})

df.rename(columns={
    'Year': 'Calendar Year', 
    'Top Animal': 'Favorite Animal', 
}, inplace=True)
df

Explanation:

• Creating DataFrame: We create a DataFrame with columns ‘Year’ and ‘Top Animal’.
• Renaming Columns: The rename method is used to change column names. The columns parameter takes a dictionary where the keys are old names and the values are new names. The inplace=True parameter makes the changes directly to the DataFrame.

Querying with Regex

Code:

import pandas as pd

df = pd.DataFrame({
  'first_name': ['Sarah', 'John', 'Kyle', 'Joe'],
  'last_name': ['Connor', 'Connor', 'Reese', 'Bonnot'],
})

df[df.last_name.str.match('.*onno.*')]

Explanation:

• Creating DataFrame: Another DataFrame is created with ‘first_name’ and ‘last_name’ columns.
• Regex Query: The str.match method is used to find rows where ‘last_name’ matches a regex pattern. Here, .*onno.* looks for any string containing ‘onno’.

Querying by Variable Value

Code:

import pandas as pd

df = pd.DataFrame({
  'first_name': ['Sarah', 'John', 'Kyle'],
  'last_name': ['Connor', 'Connor', 'Reese'],
})

foo = 'Connor'
df.query('last_name == @foo')

Explanation:

• Creating DataFrame: We create a DataFrame with first and last names.
• Variable Value Query: We set a variable foo to ‘Connor’ and use df.query to filter rows where ‘last_name’ equals the value of foo. The @ symbol is used to reference the variable in the query string.

Variable as Column Name

Code:

import pandas as pd

df = pd.DataFrame({
  'first_name': ['Sarah', 'John', 'Kyle'],
  'last_name': ['Connor', 'Connor', 'Reese'],
})

column_name = 'first_name'
df.query(f"`{column_name}` == 'John'")

Explanation:

• Creating DataFrame: Same DataFrame creation as before.
• Dynamic Column Name Query: We set column_name to ‘first_name’ and use it in a query to filter rows where the ‘first_name’ column equals ‘John’. The {} braces are used for string interpolation.

Query by Timestamp

Code:

import pandas as pd

df = pd.DataFrame({
  'time': ['2022-09-14 00:52:00-07:00', '2022-09-14 00:52:30-07:00', 
           '2022-09-14 01:52:30-07:00'],
  'letter': ['A', 'B', 'C'],
})
df['time'] = pd.to_datetime(df.time)

df.query('time >= "2022-09-14 00:52:30-07:00"')

Explanation:

• Creating DataFrame: This DataFrame has ‘time’ and ‘letter’ columns.
• Converting to Datetime: The ‘time’ column is converted to datetime format using pd.to_datetime.
• Timestamp Query: We filter rows where the ‘time’ column is greater than or equal to a specific timestamp.

Filtering by Time Range

Code:

import pandas as pd

df = pd.DataFrame({
  'time': ['2022-09-14 00:52:00-07:00', '2022-09-14 00:52:30-07:00', 
           '2022-09-14 01:52:30-07:00'],
  'letter': ['A', 'B', 'C'],
})
df['time'] = pd.to_datetime(df.time)

begin_ts = '2022-09-14 00:52:00-07:00'
end_ts = '2022-09-14 00:54:00-07:00'

df.query('@begin_ts <= time < @end_ts')

Explanation:

• Creating DataFrame: Same as before.
• Time Range Query: We define begin_ts and end_ts as the start and end timestamps and use them in a query to filter rows within this range.

Filtering by DatetimeIndex with .loc[]

Code:

import pandas as pd

df = pd.DataFrame({
  'time': ['2022-09-14 00:52:00-07:00', '2022-09-14 00:52:30-07:00', 
           '2022-09-14 01:52:30-07:00'],
  'letter': ['A', 'B', 'C'],
})
df['time'] = pd.to_datetime(df.time)
df.set_index('time', inplace=True)

df.loc['2022-09-14':'2022-09-14 00:53']

Explanation:

• Creating DataFrame: Same as before.
• Setting Index: We set the ‘time’ column as the index using set_index.
• DatetimeIndex Query: Using .loc[], we filter rows by specifying a time range.

Filtering by TimeDelta

Code:

import pandas as pd

df = pd.DataFrame({
  'time': ['2022-09-14 00:52:00-07:00', '2022-09-14 00:52:30-07:00', 
           '2022-09-14 01:52:30-07:00'],
  'letter': ['A', 'B', 'C'],
})
df['time'] = pd.to_datetime(df.time)

def rows_in_time_range(df, time_column, start_ts_str, timedelta_str):
  start_ts = pd.Timestamp(start_ts_str).tz_localize('US/Pacific')
  end_ts = start_ts + pd.to_timedelta(timedelta_str)
  return df.query("@start_ts <= {0} < @end_ts".format(time_column))

rows_in_time_range(df, 'time', '2022-09-14 00:00', '52 minutes 31 seconds')

Explanation:

• Creating DataFrame: Same as before.
• TimeDelta Query Function: This function filters rows within a specific time range calculated using start_ts_str and timedelta_str.

Describing Timestamp Values

Code:

import pandas as pd

df = pd.DataFrame({
  'time': ['2022-09-14 00:52:00-07:00', '2022-09-14 00:52:30-07:00', 
           '2022-09-14 01:52:30-07:00'],
  'letter': ['A', 'B', 'C'],
})
df['time'] = pd.to_datetime(df.time)

df['time'].describe(datetime_is_numeric=True)

Explanation:

• Creating DataFrame: Same as before.
• Descriptive Statistics: The describe method with datetime_is_numeric=True provides descriptive statistics for the ‘time’ column, treating it as numeric.

Exploding a Dictionary Column

Code:

import pandas as pd

df = pd.DataFrame({
  'date': ['2022-09-14', '2022-09-15', '2022-09-16'],
  'letter': ['A', 'B', 'C'],
  'dict' : [{ 'fruit': 'apple', 'weather': 'aces'},
            { 'fruit': 'banana', 'weather': 'bad'},
            { 'fruit': 'cantaloupe', 'weather': 'cloudy'}],
})

pd.concat([df.drop(['dict'], axis=1), df['dict'].apply(pd.Series)], axis=1)

Explanation:

• Creating DataFrame: This DataFrame includes a column with dictionary
• Column with Dictionaries: The DataFrame includes a ‘dict’ column where each cell is a dictionary.
• Exploding the Dictionary: Using df['dict'].apply(pd.Series) converts the dictionaries into separate columns. pd.concat is then used to concatenate these new columns back to the original DataFrame, dropping the original ‘dict’ column.

Extracting Values with Regex

Code:

import pandas as pd

df = pd.DataFrame({
  'request': ['GET /index.html?baz=3', 'GET /foo.html?bar=1'],
})

df['request'].str.extract('GET /([^?]+)\?', expand=True)

Explanation:

• Creating DataFrame: A DataFrame with a ‘request’ column containing HTTP request strings.
• Regex Extraction: The str.extract method uses a regex pattern to extract parts of the string. Here, GET /([^?]+)\? extracts the path of the request (everything between ‘GET /’ and ‘?’).

Convert String to Timestamp

Code:

import pandas as pd

pd.Timestamp('9/27/22').tz_localize('US/Pacific')

Explanation:

• String to Timestamp (Date Only): Converts a date string to a timestamp and localizes it to the ‘US/Pacific’ timezone.

Code (Including Time):

import pandas as pd

pd.Timestamp('9/27/22 06:59').tz_localize('US/Pacific')

Explanation:

• String to Timestamp (Including Time): Converts a date and time string to a timestamp and localizes it to the ‘US/Pacific’ timezone.

Creating a TimeDelta

Code:

import pandas as pd

pd.to_timedelta(1, unit='h')

Explanation:

• Basic TimeDelta: Creates a Timedelta object representing 1 hour.

Code (More Complex):

import pandas as pd

pd.Timedelta(days=2)

Explanation:

• Days TimeDelta: Creates a Timedelta object representing 2 days.

Code (Even More Complex):

import pandas as pd

pd.Timedelta('2 days 2 hours 15 minutes 30 seconds')

Explanation:

• Detailed TimeDelta: Creates a Timedelta object from a string representing 2 days, 2 hours, 15 minutes, and 30 seconds.

Replacing NaN Values

Code:

import numpy as np
import pandas as pd

df = pd.DataFrame({
  'dogs': [5, 10, np.nan, 7],
})

df['dogs'].replace(np.nan, 0, regex=True)

Explanation:

• Creating DataFrame: A DataFrame with a ‘dogs’ column containing some NaN values.
• Replacing NaN: The replace method replaces NaN values with 0. The regex=True parameter is not necessary here, but it doesn’t affect the outcome in this case.

Dropping Duplicate Rows

Code:

import pandas as pd

df = pd.DataFrame({
  'first_name': ['Sarah', 'John', 'Kyle', 'Joe'],
  'last_name': ['Connor', 'Connor', 'Reese', 'Bonnot'],
})
df.set_index('last_name', inplace=True)

df.loc[~df.index.duplicated(), :]

Explanation:

• Creating DataFrame: A DataFrame with ‘first_name’ and ‘last_name’ columns.
• Setting Index: The ‘last_name’ column is set as the index using set_index.
• Dropping Duplicates: ~df.index.duplicated() creates a boolean mask where False corresponds to duplicated index values. The .loc[] method uses this mask to select only unique rows.

With these explanations, you should have a solid understanding of each code snippet and how to use these techniques in your data analysis projects.

What is pandas and NumPy in Python?

Pandas and NumPy: The Dynamic Duo of Data

So, you’re diving into Python and everyone’s raving about Pandas and NumPy. What’s the deal with these two?

NumPy: The Brains Behind the Numbers

NumPy is like the foundation of the entire Python data world. It stands for “Numerical Python,” and it’s all about crunching numbers, working with arrays, and delving into the world of math. Think of it as the strong backbone that makes everything else work seamlessly.

With NumPy, you can perform some pretty impressive number magic. Need to do complex math operations on arrays? NumPy is your go-to. Dealing with multi-dimensional data? NumPy has got your back.

Pandas: The Data Whisperer

Now, let’s talk about Pandas. This library is all about mastering data like a pro. It’s like the wizard of data manipulation, transforming messy datasets into gold.

Pandas builds upon the foundation of NumPy, adding even more power to the mix. It introduces two key players: DataFrame and Series. A DataFrame is like a fancy table where you can store and analyze your data, while a Series is like a single column of that table.

With Pandas, you’re in control of your data. Need to slice it, dice it, filter it, or group it? Pandas has your back with a whole bag of tricks.

Why They’re a Perfect Match

So, why do Pandas and NumPy go together like peanut butter and jelly? Well, Pandas heavily relies on NumPy behind the scenes. A lot of Pandas’ data wizardry is powered by NumPy’s efficient array processing.

Moreover, they complement each other seamlessly. You can effortlessly switch between Pandas DataFrames and NumPy arrays, combining their strengths to tackle any data challenge.

Are pandas easy to learn?

Although Pandas can be difficult at first, there are plenty of resources available for beginners, a helpful Python community, and chances for hands-on learning.

By staying committed and persistent, anyone interested in data can easily become proficient in Pandas.

Is pandas a library or package?

Pandas is an incredible library in Python. In the Python world, the terms “library” and “package” are often used interchangeably to describe a group of modules or functions that enhance the capabilities of Python.

These libraries/packages consist of reusable code that serves specific purposes like data manipulation, mathematical operations, web development, and more.

Pandas, in particular, is an exceptional open-source library for data analysis and manipulation in Python. It offers high-level data structures like DataFrame and Series, along with a vast array of tools for cleaning, exploring, manipulating, and analyzing data.

To sum it up, while both “library” and “package” can be used to describe collections of code in Python, “library” is the more commonly used term when referring to Pandas.

Conclusion

To sum up, Pandas is an incredibly useful library for data analysis and manipulation in Python. Although it may seem challenging at first, there are plenty of resources available to help beginners understand its fundamentals. As you gain more experience with Python, you’ll find that learning Pandas becomes easier.

It’s important to practice regularly and start with simple tasks before moving on to more complex operations. This approach allows you to gradually build your skills and confidence over time.

The Python community is also very supportive and offers many opportunities for collaboration and assistance.

If you ever need guidance or advice, you can rely on the community to help you out. Additionally, Pandas’ integration with other Python libraries and tools makes it even more versatile and useful for various data-related tasks and projects.

Although Pandas may appear intimidating initially, it is a foundational library in the Python data ecosystem. With its extensive capabilities and strong community support, investing time in mastering Pandas is definitely worthwhile if you want to excel in data analysis and manipulation.