Search results “Python data analysis pandas”

Data science is the fastest-growing segment of the Python community and Python is the de-facto language in data science. Well-known speaker and author Matt Harrison joins us to discuss pandas, the hot-topic Python library for data science, and how to use it in a sample application.
Matt provides a walkthrough through some of the features of pandas: data ingestion, cleaning, and adding columns. As a demo application to show Python and data science, Matt will analyze bitcoin price data, making a simple model to show whether the price of bitcoin would rise or fall.
Contents
03:34 Introduction to Jupyter
06:43 pandas and matplotlib
08:20 Read/view bitcoin csv data
12:24 Setting index
15:00 Aggregation
17:10 Slicing
18:40 Computed columns with assign
21:30 Questions
33:10 Random forest
40:00 ROC Curve
42:55 PyCharm
49:20 Questions
Matt will put some material in his GitHub account: https://github.com/mattharrison

Views: 23033
JetBrainsTV

Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library.
At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds.
Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/
Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/
Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/

Views: 441024
sentdex

Python data analysis / data science tutorial. Let’s go!
For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata
Sample data and sample code: https://www.csdojo.io/data
My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8
Also, keep in touch on Twitter: https://twitter.com/ykdojo
And Facebook: https://www.facebook.com/entercsdojo
Outline - check the comment section for a clickable version:
0:37: Why data visualization?
1:05: Why Python?
1:39: Why Matplotlib?
2:23: Installing Jupyter through Anaconda
3:20: Launching Jupyter
3:41: DEMO begins: create a folder and download data
4:27: Create a new Jupyter Notebook file
5:09: Importing libraries
6:04: Simple examples of how to use Matplotlib / Pyplot
7:21: Plotting multiple lines
8:46: Importing data from a CSV file
10:46: Plotting data you’ve imported
13:19: Using a third argument in the plot() function
13:42: A real analysis with a real data set - loading data
14:49: Isolating the data for the U.S. and China
16:29: Plotting US and China’s population growth
18:22: Comparing relative growths instead of the absolute amount
21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata

Views: 127332
CS Dojo

( Python Training : https://www.edureka.co/python )
This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014.
This Python Pandas tutorial video helps you to learn following topics:
1. What is Data Analysis?
2. What is Pandas?
3. Pandas Operations
4. Use-case
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Subscribe to our channel to get video updates. Hit the subscribe button above.
#Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonPandas
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at [email protected]
Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 125618
edureka!

There are audio issues with this video that cannot be fixed. We recommend listening to the tutorial without headphones to minimize the buzzing sound.
Tutorial information may be found at https://scipy2017.scipy.org/ehome/220975/493423/
Data Science and Machine learning have been synonymous with languages like Python. Libraries like numpy and Pandas have become the de facto standard when working with data.
The DataFrame object provided by Pandas gives us the ability to work with heterogeneous unstructured data that is commonly used in "real world" data.
New learners are often drawn to Python and Pandas because of all the different and exciting types of models and insights the language can do and provide, but are awestruck when faced with the initial learning curve.
This tutorial aims to guide the learner from using spreadsheets to using the Pandas DataFrame.
Not only does moving to a programming language allow the user to have a more reproducible workflow, but as datasets get larger, some cannot even be opened in a spreadsheet program. The goal is to have an absolute beginner proficient enough with Pandas that they can start working with data in Python.
We will cover how to load and view our data. Then, some basic methods to do quick visualizations of our data for exploratory data analysis. We will then work on combining and working multiple datasets (concatenating and merging), and introduce what Dr. Hadley Wickham has coined "tidy data". Tidy data is an important concept because the process of tidying data will fix a host of data problems that are needed to perform analysis. We then cover functions and applying methods to our data with a focus on data cleaning, and how we can use the concept of split-apply-combine (groupby) to summarize or reduce our data.
Finally, we cover the basics of string manipulation and how to use it to clean data before briefly covering the role of Pandas in analysis packages such as scikit learn. The tutorial will with a fitted model.
The goal is to get people familiar with Python and Pandas so they can learn and explore many other parts of the Python ecosystem.

Views: 47581
Enthought

Wes McKinney
The tutorial will give a hands-on introduction to manipulating and analyzing large and small structured data sets in Python using the pandas library. While the focus will be on learning the nuts and bolts of the library's features, I als

Views: 290386
Next Day Video

Code: https://github.com/codebasics/py/tree/master/pandas/2_dataframe_basics
This pandas tutorial covers basics on dataframe. DataFrame is a main object of pandas. It is used to represent tabular data (with rows and columns). This tutorial will go over,
1) What is dataframe?
2) Create dataframe from csv file and python dictionary
3) Dealing with rows and columns
4) Operations: mean, max, std, describe
5) Conditional selection
6) set_index function and usefulness of it
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Google +: https://plus.google.com/106698781833798756600

Views: 111339
codebasics

The advanced series has begun! For this exercise, we're using Python
3 and the Pandas library to take raw data from a property's PMS and visualize it in a meaningful way.

Views: 1995
Hotel Revenue Management

PyData Carolinas 2016
This tutorial aims to expose future data analysis with the Python Pandas library. The tutorial is meant for absolute beginners to Pandas and Python as a programming language. We will begin with loading tabular data and various ways to calculate summary statistics and visualize data. Next, we will learn various ways to join multiple datasets, and how we can work with missing values etc.
The purpose of the tutorial is to expose users to Pandas for data analysis and move users into a more reproducible analysis workflow compared to spreadsheet programs. The tutorial will begin with loading in tabular data and various ways to view columns and rows of data. The initial part of the tutorial will show users how to load in data and quickly create descriptive statistical plots. Next, we will cover more of pandas internal data structures. This will cover some fundamental knowledge about Python as a programming language, mainly object methods. Finally, before showing users basic data cleaning examples, we will cover data visualizations using matplotlib, seaborn, and pandas itself.
The next section of the tutorial will show learners how to assemble and merge multiple datasets, and how to work with missing values. Lastly, before we get to fitting models, I will go over how to recode variables for analysis.
The main purpose is to show users how to use Pandas, and not how to fit machine learning models. However, I show a simple model at the end so users see how data cleaning and analysis all fit together in a workflow.

Views: 16770
PyData

www.Stats-Lab.com | Data Analysis with Python | Working with Pandas Dataframes (Iris Exercise 1b)
1) Renaming a Column
2) Deleting a Column
3) Creating a New Columns

Views: 3654
Dragonfly Statistics

In this Data analysis with Python and Pandas tutorial, we're going to clear some of the Pandas basics. Data prior to being loaded into a Pandas Dataframe can take multiple forms, but generally it needs to be a dataset that can form to rows and columns.
Text-version and sample code for this tutorial: http://pythonprogramming.net/basics-data-analysis-python-pandas-tutorial/
Python dictionaries tutorial: http://pythonprogramming.net/dictionaries-tutorial-python-3/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 189843
sentdex

Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and efficiently clean, analyze, and visualize data of varying sizes and types. In this seminar, we'll learn how to use pandas to extract meaningful insights and recommendations from real-world datasets.

Views: 80693
CS50

Description
Data analytics in Python benefits from the beautiful API offered by the pandas library. With it, manipulating and analysing data is fast and seamless. In this workshop, we'll take a hands-on approach to performing an exploratory analysis in pandas. We'll begin by importing some real data. Then, we'll clean it, transform it, and analyse it, finishing with some visualisations.
Introduction
In this hands-on workshop, we'll walk through the exploratory analysis of real-world data. Datasets are often messy, full of holes and inconsistencies, and a data scientist or analyst may spend a large fraction of their time cleaning and preparing data.
Fortunately, pandas makes a lot of this fairly trivial. It allows the user to import data from all sorts of different sources, and then manipulate the powerful DataFrame object. Analytics with pandas are human-friendly.
Workshop
Pulling in the data
Starting with some data in CSV form, we'll look at the general properties of our dataset. What columns do we have; what kind of values are contained in them ? We'll identify problematic fields, and join two datasets to make one complete dataframe.
Cleaning
We've identified problems with our data, and now it's time to correct them. We'll fill in missing values, drop irrelevant rows, and fix incorrect datatypes.
Transforming the data
Next, we'll standardise some numerical fields where we're looking for deviations rather than absolute values, and derive some new columns based on the data we have.
Visualisation
Throughout, we'll be generating visualisations, to guide us in where to go next.
Prerequisites
You'll need to be fairly comfortable working with Python. We won't be doing anything overly complicated, but having a grasp of Python syntax is expected.
Laptop
If you want to follow along, please have a working Python setup, with pandas and matplotlib installed. Aim for a recent version of pandas. If you're unsure what to install, I recommend getting Python 3 through Anaconda : https://www.continuum.io/downloads - this distribution comes with everything you need and is very friendly.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Views: 7792
PyData

This video will explain how to select subgroup of rows based on logical condition.
Visit complete course on Data Science with Python :
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090
For All other visit my udemy profile at :
https://www.udemy.com/user/ankitmistry/

Views: 4920
MyStudy

http://alphabench.com/data/data-analysis-python.html
***NOTE pandas_datareader is no longer able to download data from Yahoo. I recommend installing fix_yahoo_finance. See the tutorial:
https://alphabench.com/data/python-fix-yahoo-finance-tutorial.html
Or, Open a command window and enter the following:
pip install fix_yahoo_finance
Once complete, open Python or start a notebook and:
import fix_yahoo_finance as fyf
data = fyf.download(stock_symbol(s), start*, end*)
*optional
Video tutorial that discusses fundamental data analysis techniques using stock price data from Amazon. Makes use of Python 3.6 and several supporting libraries including Pandas, Pandas Datareader and Matplotlib.
To install an environment similar to that used here install the Anaconda scientific platform, free download from:
https://www.continuum.io/
The jupyter notebook used in this tutorial can be downloaded by visiting:
https://nbviewer.jupyter.org/url/alphabench.com/data/Python-Basic-Data-Analysis.ipynb

Views: 6941
Matt Macarty

https://github.com/mnd-af/src/blob/master/2017/06/04/Uber%20Data%20Analysis.ipynb

Views: 14291
MandarinaCS

Hi Guys checkout my udemy course at just 9.99$
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=KNOWLEDGE_IS_POWER
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Visit complete course on Data Science with Python :
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090
For All other visit my udemy profile at :
https://www.udemy.com/user/ankitmistry/
This tutorial will explain how to select individual row, or column and cell or group of cell of DataFrame object in python pandas.
import pandas as pd
# # Read File
df = pd.read_csv('train.csv')
df.head()
# # Select one column
df['Name']
# # Select more than one column
df.columns
df[['Name','Fare']]
# # Select one Row or more than one row
df.iloc[5]
df.head(6)
df.iloc[423:425]
# # Select Cell
df.head()
df.iloc[1,3]
df.iloc[500:600,3]
For full course on Data Science with python pandas at just 9.99$ check
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=KNOWLEDGE_IS_POWER

Views: 14153
MyStudy

In this video, we'll cover the data science pipeline from data ingestion (with pandas) to data visualization (with seaborn) to machine learning (with scikit-learn). We'll learn how to train and interpret a linear regression model, and then compare three possible evaluation metrics for regression problems. Finally, we'll apply the train/test split procedure to decide which features to include in our model.
Download the notebook: https://github.com/justmarkham/scikit-learn-videos
pandas installation instructions: http://pandas.pydata.org/pandas-docs/stable/install.html
seaborn installation instructions: http://seaborn.pydata.org/installing.html
Longer linear regression notebook: https://github.com/justmarkham/DAT5/blob/master/notebooks/09_linear_regression.ipynb
Chapter 3 of Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/
Videos related to Chapter 3: https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
Quick reference guide to linear regression: https://www.dataschool.io/applying-and-interpreting-linear-regression/
Introduction to linear regression: http://people.duke.edu/~rnau/regintro.htm
pandas Q&A video series: https://www.dataschool.io/easier-data-analysis-with-pandas/
pandas 3-part tutorial: http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
pandas read_csv documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
pandas read_table documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html
seaborn tutorial: http://seaborn.pydata.org/tutorial.html
seaborn example gallery: http://seaborn.pydata.org/examples/index.html
WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS:
1) WATCH my scikit-learn video series:
https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
2) SUBSCRIBE for more videos:
https://www.youtube.com/dataschool?sub_confirmation=1
3) JOIN "Data School Insiders" to access bonus content:
https://www.patreon.com/dataschool
4) ENROLL in my Machine Learning course:
https://www.dataschool.io/learn/
5) LET'S CONNECT!
- Newsletter: https://www.dataschool.io/subscribe/
- Twitter: https://twitter.com/justmarkham
- Facebook: https://www.facebook.com/DataScienceSchool/
- LinkedIn: https://www.linkedin.com/in/justmarkham/

Views: 114759
Data School

https://alphabench.com/data/pandas-stock-analysis-tutorial.html
***NOTE pandas_datareader is no longer able to download data from Yahoo. I recommend installing fix_yahoo_finance. Open a command window and enter the following, see the video for installation and usage:
https://youtu.be/eSpH6fPd5Yw
pip install fix_yahoo_finance
Once complete, open Python or start a notebook and:
import fix_yahoo_finance as fyf
data = fyf.download(stock_symbol(s), start*, end*)
*optional
Follow-on video for Python pandas moving averages and rolling statistics. The first part can be found here:
https://alphabench.com/data/pandas-rolling-tutorial.html
Download a working copy of the Jupyter notebook here:
https://alphabench.com/data/pandas-stock-analysis-tutorial.html
Covers some basic stock price movement analysis, in the form of data transformation, adding columns to DataFrame and data visualization with histogram and an interesting take on scatter plots.

Views: 4103
Matt Macarty

This video will explain Basic of Pandas Dataframe datastructure which read googgle stock data from google finance api.
How to display and plot, of dataframe object..
For full course on Data Science with python pandas at just 9.99$ check
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=KNOWLEDGE_IS_POWER
Visit complete course on Data Science with Python :
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090
For All other visit my udemy profile at :
https://www.udemy.com/user/ankitmistry/

Views: 14223
MyStudy

Description
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series. The tutorial provides a compact introduction to Pandas for beginners for I/O, data visualisation, statistical data analysis and aggregation within Jupiter notebooks.
Abstract
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series.
The tutorial provides a compact introduction to Pandas for beginners:
-reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…)
-data visualisation
-statistical data analysis and aggregation.
-work with built-in data visualisation
-inner-mechanics of Pandas: Data-Frames, Data-Series & Numpy.
-working and making the most of indexes.
-how to mangle, reshape and pivot
The tutorial will be provided as Jupiter notebooks.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Views: 6540
PyData

In this second part of the SQL for data science video I show you how to use Pandas to parse several large CSV files and load them in to a single SQLite database. This allows you to perform analysis in datasets that have several million rows.
Learn how to setup jupyter notebook:
https://www.youtube.com/watch?v=Q73ADVZCqSU
Part 1: Introduction to SQL
https://www.youtube.com/watch?v=7NSr6C8IhCo

Views: 1739
MandarinaCS

Learn the basics of Python's spread sheet library, Pandas in the tutorial.

Views: 14838
Joe James

In this end to end example we web scrape the HTML of this class schedule off of this website: https://ischool.syr.edu/classes/ into a pandas dataframe. From there we extract a feature column for which classes are Undergraduate versus Graduate, then we finish by finding the Undergraduate classes on Fridays or at 8AM.
Like all End-To-End examples the program is written organically piece by piece until complete. I make mistakes and figure things out as I go.
You can download the code for this example on GitHub: https://github.com/IST256/learn-python/tree/master/content/lessons/12/End-To-End-Example

Views: 3274
Michael Fudge

In this episode of AI Adventures, Yufeng explores the fascinating world of pandas, an open-source python library that provides easy to use, high-performance data structures and data analysis tools.
Associated Medium post "Wrangling data with Pandas": https://goo.gl/txENbw
Pandas: http://pandas.pydata.org/
Watch more episodes of AI Adventures: https://goo.gl/UC5usG
Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51

Views: 29801
Google Cloud Platform

Katharine Jarmul teaches you about joins using Pandas. More details about the course, as well as more free lessons, can be found at http://oreil.ly/2bF9KfM.
This video teaches you how to use a join to join data frames to one another. You'll also review what joins do and the types of joins that are available.
Follow @strataconf for more data news: http://oreil.ly/2bigd1Z

Views: 9436
O'Reilly - Video Training

Link to the code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Time_Series/Part1_Time_Series_Data_BasicPlotting.ipynb
Viewing Pandas DataFrame, Adding Columns in Pandas, Plotting Two Pandas Columns, Sampling Using Pandas, Rolling mean in Pandas (Smoothing), Subplots, Plotting against Date (numpy.datetime), Filtering DataFrame in Pandas, Simple Joins, and Linear Regression.
This tutorial is mostly focused on manipulating time series data in the Pandas Python Library.

Views: 25004
Michael Galarnyk

DragonflyStats.github.io | Pydata | Frequency Tables with Pandas

Views: 7519
Dragonfly Statistics

Data analytics in Python benefits from the beautiful API offered by the pandas library. With it, manipulating and analysing data is fast and seamless. In this workshop, we'll take a hands-on approach to performing an exploratory analysis in pandas. We'll begin by importing some real data. Then, we'll clean it, transform it, and analyse it, finishing with some visualisations.
EVENT:
PyData Seattle 2017
SPEAKER:
Quentin Caudron
PERMISSIONS:
PyData provided Coding Tech with the permission to republish this video.
CREDITS:
Original video source: https://www.youtube.com/watch?v=F7sCL61Zqss

Views: 4027
Coding Tech

This is a review of the fantastic Python for Data Analysis. I learnt a lot from this book by Wes McKinney. Its section on IPython is excellent and it explains Numpy extremely well: two chapters are dedicated to Numpy covering the basics and advanced topics. It really helps you to visualize numpy arrays. The Pandas coverage is excellent and you learn what a powerful tool Pandas can be. I always use it for my data analysis, usually in a jupyter notebook and I love what it can do. If you are new to data analysis and are looking for an introductory book to explain how to do it in Python, I haven't seen a better one than this.
Buy it from Amazon (USA) https://amzn.to/2I2TB1H
(UK) https://amzn.to/2KFwmg9 (2nd Edition books) (affiliate links)

Views: 6369
Python Programmer

In this python for Data science tutorial, you will learn how to do Pearson correlation Analysis and parametric Methods using pandas and scipy in python Jupyter notebook. (Anaconda).
Download Link for Cars Data Set:
https://www.4shared.com/s/fWRwKoPDaei
Download Link for Enrollment Forecast:
https://www.4shared.com/s/fz7QqHUivca
Download Link for Iris Data Set:
https://www.4shared.com/s/f2LIihSMUei
https://www.4shared.com/s/fpnGCDSl0ei
Download Link for Snow Inventory:
https://www.4shared.com/s/fjUlUogqqei
Download Link for Super Store Sales:
https://www.4shared.com/s/f58VakVuFca
Download Link for States:
https://www.4shared.com/s/fvepo3gOAei
Download Link for Spam-base Data Base:
https://www.4shared.com/s/fq6ImfShUca
Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca
Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Views: 5615
TheEngineeringWorld

Welcome to part 12 of the Data Analysis with Python and Pandas tutorial series. In this tutorial, we're goign to talk briefly on the handling of erroneous/outlier data. Just because data is an outlier, it does not mean it is erroneous. A lot of times, an outlier data point can nullify a hypothesis, so the urge to just get rid of it can be high, but this isn't what we're talking about here.
What would an erroneous outlier be? An example I like to use is when measuring fluctuations in something like, say, a bridge. As bridges carry weight, they can move a bit. In storms, that can wiggle about a bit, there is some natural movement. As time goes on, and supports weaken, the bridge might move a bit too much, and eventually need to be reinforced. Maybe we have a system in place that constantly measures fluctuations in the bridge's height.
Text based tutorial and sample code: http://pythonprogramming.net/comparison-operators-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 21990
sentdex

Visit complete course on Data Science with Python :
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This video will explain how to calculate moving average of time series data with python pandas library rolling function.
# # Calculate Moving average of Time series data
# # 1, 4, 7, 9, 2, 4, 6, 7, 8
# ## (NAN+1)/2 (1+4)/2 (4+7)/2 (7+9)/2 (9+2)/2
# In[4]:
import pandas as pd
# In[7]:
data1 = {'data':[1, 4, 7, 9, 2, 4, 6, 7, 8]}
# In[8]:
data1
# In[9]:
df = pd.DataFrame(data1)
# In[10]:
df
# In[12]:
df1 = df.rolling(3).mean()
df1
# In[13]:
import matplotlib.pyplot as plt
# In[14]:
plt.plot(df['data'])
plt.plot(df1['data'])
plt.show()

Views: 4261
MyStudy

Welcome to another data analysis with Python and Pandas tutorial series, where we become real estate moguls. In this tutorial, we're going to be covering the application of various rolling statistics to our data in our dataframes.
One of the more popular rolling statistics is the moving average. This takes a moving window of time, and calculates the average or the mean of that time period as the current value. In our case, we have monthly data. So a 10 moving average would be the current value, plus the previous 9 months of data, averaged, and there we would have a 10 moving average of our monthly data. Doing this is Pandas is incredibly fast. Pandas comes with a few pre-made rolling statistical functions, but also has one called a rolling_apply. This allows us to write our own function that accepts window data and apply any bit of logic we want that is reasonable. This means that even if Pandas doesn't officially have a function to handle what you want, they have you covered and allow you to write exactly what you need. Let's start with a basic moving average, or a rolling_mean as Pandas calls it. You can check out all of the Moving/Rolling statistics from Pandas' documentation.
Text tutorial and sample code: http://pythonprogramming.net/rolling-statistics-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 28979
sentdex

Welcome to Part 6 of the Data Analysis with Python and Pandas tutorial series. In this part, we're going to talk about joining and merging dataframes, as another method of combining dataframes. In the previous tutorial, we covered concatenation and appending.
Joining/merging tutorial text and sample code: http://pythonprogramming.net/join-merge-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 79501
sentdex

Welcome to Part 5 of our Data Analysis with Python and Pandas tutorial series. In this tutorial, we're going to be covering how to combine dataframes in a variety of ways.
In our case with real estate investing, we're hoping to take the 50 dataframes with housing data and then just combine them all into one dataframe. We do this for multiple reasons. First, it is easier and just makes sense to combine these, but also it will result in less memory being used. Every dataframe has a date and value column. This date column is repeated across all the dataframes, but really they should all just share the one, effectively nearly halving our total column count.
When combining dataframes, you might have quite a few goals in mind. For example, you may want to "append" to them, where you may be adding to the end, basically adding more rows. Or maybe you want to add more columns, like in our case. There are four major ways of combining dataframes, which we'll begin covering now. The four major ways are: Concatenation, joining, merging, and appending. We'll begin with Concatenation.
Sample code and text-based version of this tutorial: http://pythonprogramming.net/concatenate-append-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 75362
sentdex

Python Pandas CookBook - Data Analysis
https://github.com/alfredessa/

Views: 3079
TechKeka

A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis

Views: 150794
APMonitor.com

In this Data Analysis with Pandas and Python tutorial series, we're going to show how quickly we can take our Pandas dataset in the dataframe and convert it to, for example, a numpy array, which can then be fed through a variety of other data analysis Python modules. The example that we're going to use here is Scikit-Learn, or SKlearn. In order to do this, you will need to install it:
pip install sklearn
From here, we're almost already done. For machine learning to take place, at least in the supervised form, we need only a couple things. First, we need "features." In our case, features are things like current HPI, maybe the GDP, and so on. Then you have "labels." Labels are assigned to the feature "sets," where a feature set is the collective GDP, HPI, and so on for any given "label." Our label, in this case, is either a 1 or a 0, where 1 means the HPI increased in the future, and a 0 means it did not.
Sample code and text-based tutorial: http://pythonprogramming.net/scikit-learn-sklearn-machine-learning-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 20347
sentdex

Learn about chart in Python in this python data visualization tutorial. explore graphing with python by describing categorical data inside Jupyterlab. This is a part of statistics with Python Tutorial series.
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Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python
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Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ
Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o
Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E
Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0
Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw
Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE
Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA
Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8
Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw
Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ
Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI
Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44
🔷🔷🔷🔷🔷🔷🔷
*** Complete Python Programming Playlists ***
* Python Data Science
https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK
* NumPy Data Science Essential Training with Python 3
https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b
* Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Python Smart Programming in Jupyter Notebook:
https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2
* Python Coding Interview:
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Views: 312
TheEngineeringWorld

Wes McKinney
In this tutorial, I'll give a brief overview of pandas basics for new users, then dive into the nuts of bolts of manipulating time series data in memory. This includes such common topics date arithmetic, alignment and join / merge method

Views: 51240
Next Day Video

Hello everyone and welcome to Part 14 of our Data Analysis with Python and Pandas for Real Estate investing tutorial series. We've come quite a long ways here, and the next, and final, macro step that we want to take here involves looking into economic indicators to see their impact on housing prices, or the HPI.
Text based version of this tutorial and sample code: http://pythonprogramming.net/economic-factors-data-analysis-python-pandas-tutorial/
There are two major economic indicators that come to mind out the gate: S&P 500 index (stock market) and GDP (Gross Domestic Product). I suspect the S&P 500 to be more correlated than the GDP, but the GDP is usually a better overall economic indicator, so I may be wrong. Another macro indicator that I suspect might have value here is the unemployment rate. If you're unemployed, you're probably not getting that mortgage. We'll see though. We've been through the process for adding more data points, so I do not see much point in dragging you all through this process. There will be one new thing to note, however. In the HPI_Benchmark() function, we're changing the "United States" column to be US_HPI. This makes a bit more sense when we're bringing in other values now.
For GDP, I couldn't find one that encompassed the full time frame. I am sure you can find a dataset, somewhere, with this data, maybe even on Quandl. Sometimes you have to do some digging. I also had trouble finding a nice long-term monthly unemployment rate. I did find an unemployment level, but we really want more of a percentage/rate, otherwise we need to divide the unemployment level by the population. We could do that if we decide unemployment rate is worth having, but we'll work with what we get first.
http://pythonprogramming.net
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Views: 15913
sentdex

PyData Dallas 2015

Views: 5763
PyData

In this python pandas tutorial you will learn how groupby method can be used to group your dataset based on some criteria and then apply analytics on each of the groups. This is similar to SQL group by. It is also called split apply combine strategy in data science.
Link for code and data used in this tutorial: https://github.com/codebasics/py/tree/master/pandas/7_group_by
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Google +: https://plus.google.com/106698781833798756600

Views: 34788
codebasics

Data Analysis with Python for Excel User Part 1 Read and Write Excel File using Pandas
In this video we are going to learn how to read excel file using pandas
Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. The language provides constructs intended to enable writing clear programs on both a small and large scale.
Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.It also supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
The next part: https://youtu.be/-QKaVu_ebVY
If you want to code in python for excel which the syntax is similar to VBA, you can go to this playlist: https://www.youtube.com/playlist?list=PL902m_5hKTbyJ4tnM0ax-7AI7VePas2QS
Our money manager android app: https://play.google.com/store/apps/details?id=com.leazzy.moneymanagerleazzy

Views: 43914
Peasy Tutorial

This video will explain Basic of Pandas Dataframe datastructure , which create dataframe from python built in datstructure dictionary.
Visit complete course on Data Science with Python :
https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090
For All other visit my udemy profile at :
https://www.udemy.com/user/ankitmistry/

Views: 2873
MyStudy

Welcome to another data analysis with Python and Pandas tutorial. In this tutorial, we're going to be talking about smoothing out data by removing noise. There are two main methods to do this. The most popular method used is what is called resampling, though it might take many other names. This is where we have some data that is sampled at a certain rate. For us, we have the Housing Price Index sampled at a one-month rate, but we could sample the HPI every week, every day, every minute, or more, but we could also resample at every year, every 10 years, and so on.
Another environment where resampling almost always occurs is with stock prices, for example. Stock prices are intra-second. What winds up happening though, is usually stock prices are resampled to minute data at the lowest for free data. You can buy access to live data, however. On a long-term scale, usually the data will be sampled daily, or even every 3-5 days. This is often done to keep the size of the data being transferred low. For example, over the course of, say, one year, intra-second data is usually in the multiples of gigabytes, and transferring all of that at once is unreasonable and people would be waiting minutes or hours for pages to load.
Using our current data, which is currently sampled at once a month, how might we sample it instead to once every 6 months, or 2 years? Try to think about how you might personally write a function that might perform that task, it's a fairly challenging one, but it can be done. That said, it's a fairly computationally inefficient job, but Pandas has our backs and does it very fast.
Sample code and text tutorial for this video: http://pythonprogramming.net/resample-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 32493
sentdex

Speaker: Kevin Markham
The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, proper data science requires careful coding, and pandas will not stop you from creating misleading plots, drawing incorrect conclusions, ignoring relevant data, including misleading data, or executing incorrect calculations.
In this tutorial, you'll perform a variety of data science tasks on a handful of real-world datasets using pandas. With each task, you'll learn how to avoid either a pandas pitfall or a data science pitfall. By the end of the tutorial, you'll be more confident that you're using pandas for good rather than evil!
Participants should have a working knowledge of pandas and an interest in data science, but are not required to have any experience with the data science workflow. Datasets will be provided by the instructor.
Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides

Views: 5527
PyCon 2018

Welcome to Part 8 of our Data Analysis with Python and Pandas tutorial series. In this part, we're going to do some of our first manipulations on the data.
Tutorial sample code and text: http://pythonprogramming.net/percent-change-correlation-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex

Views: 48903
sentdex

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