data manipulation for palmerpenguins using pandas
pandas defines fundamental structures for working with data and endows them with methods that facilitate operations such as: reading in data, adjusting indices, selecting, filtering, sorting, grouping, re-ordering
0:00 The Palmer Penguins Dataset Introduction
2:35 import pandas as pd
3:00 load in palmer penguins data set using pd.read_csv and url
3:25 rows and columns for pandas library
4:01 df.shape or penguins.shape
4:28 df.to_csv(' ') or penguins.to_csv('penguins2.csv') write out csv file to content folder in google colab
7:16 df.head(10) or penguins.head(10) print out first 10 rows of pandas dataframe
7:48 penguins.count() or df.count() finding missing values
8:38 print(penguins.isna().sum()) count missing values
9:11 .info() with dtypes or data types
9:38 .nunique() count unique numbers in each column
11:15 df.describe() or penguins.describe() basic descriptive statistics
12:30 print out an identified column using python pandas library
14:17 df.sort_index(axis =1, ) sorting dataframe by axis or alphabet ordering of column names
16:05 df.sort_values order rows by values of a column using sort command in pandas library
18:22 compute summary statistics using pandas
19:37 Subset Variables - by columns
20:50 iloc pandas selection
32:17 select multiple columns using pandas
33:06 loc pandas selection
40:09 check a condition is satisfied in pandas dataframe
41:24 Filtering dataframe with Logical Operators in pandas
45:00 sample rows
45:29 groupby() from pandas or summarize by group
Google Colab:
https://colab.research.google.com/dri...
Mastering Data Manipulation with Pandas: Exploring the Palmer Penguins Dataset
In this tutorial, we're going to do a dive deep into the Palmer Penguins dataset and test drive a bit the potential of Pandas as we parse and explore the data. From loading and cleaning to summarizing and subsetting, we'll provide a fairly comprehensive overview
0:00 The Palmer Penguins Dataset Introduction:
Let's kick things off by introducing you to the Palmer Penguins dataset.
2:35 Importing Pandas:
We'll start by importing the Pandas library as our trusty sidekick for data manipulation.
3:00 Loading the Dataset:
Using pd.read_csv and a URL, we'll load in the Palmer Penguins dataset. Get ready to dive into the data!
3:25 Rows and Columns:
A quick look at the foundational concept of rows and columns in Pandas.
4:01 Exploring the Shape:
Learn how to get the shape of the dataset using df.shape or penguins.shape.
4:28 Saving Data:
Discover how to save your processed data back to a CSV file using df.to_csv() or penguins.to_csv().
7:16 Printing the First Rows:
Peek into the dataset by printing the first 10 rows using df.head(10) or penguins.head(10).
7:48 Counting Missing Values:
Uncover missing data with penguins.count() or df.count(). Detect and conquer those gaps!
8:38 Identifying Missing Values:
Take the detective's route by using .isna().sum() to count the missing values.
9:11 Exploring Data Types:
Get a quick overview of column data types with .info(). Let's understand the data!
9:38 Counting Unique Values:
Learn how to count unique values in each column using .nunique().
11:15 Descriptive Statistics:
Get a statistical summary of the data with df.describe() or penguins.describe(). Understand your data's essence!
12:30 Print Out Columns:
Discover how to extract and print out a specific column using Pandas.
14:17 Sorting Columns:
Master the art of sorting columns by axis or in alphabetical order using df.sort_index().
16:05 Sorting Rows:
Explore sorting rows by the values of a specific column using df.sort_values().
18:22 Summary Statistics:
Learn to compute summary statistics that reveal key insights hidden within the data.
19:37 Subset Variables:
Discover how to subset variables (columns) and understand the power it holds.
20:50 Pandas Selection with iloc:
Unlock the magic of .iloc to precisely select rows and columns.
32:17 Selecting Multiple Columns:
Learn the art of selecting multiple columns using Pandas.
33:06 Pandas Selection with loc:
Explore the capabilities of .loc for location-based selection.
40:09 Conditional Selection:
Harness the power of Pandas to check if conditions are met within your DataFrame.
41:24 Filtering with Logical Operators:
Discover how to filter your DataFrame using logical operators for precision.
45:00 Sample Rows:
Explore the wonder of extracting random sample rows from your dataset.
45:29 Grouping and Summarizing:
Learn about the groupby() function to effectively summarize data by groups.