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Clement Lanthier, the president and CEO of the Calgary Zoo joins the Morning News to discuss the organization's decision to send its pandas back to China after concerns arise from gathering enough fresh bamboo.

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Chinese President Xi Jinping on Wednesday agreed to send a pair of pandas to Finland, the latest installment of his country's "panda diplomacy" that makes use of one of China's cutest natural assets to gain international concessions. Finland will receive a pair of pandas from China.Chinese President Xi Jinping made the announcement after talks with Finland's president Wednesday.The deal is part of China's 'panda diplomacy' that makes use of one of China's cutest natural assets to gain international concessions.The two countries signed protocols, including finalizing an agreement on panda protection as well as sending the pandas to a Finnish nature reserve.Officials have been talking about conditions for sending pandas to Finland since December 2014.Ahtari Zoo nature reserve said the animals will be loaned for 15 years and are expected to arrive by the end of the year.For the AP, I'm JL from the Rivet World Desk.

Smart Pandas Favorites

The truth about the Android Skype vulnerability, new Windows 8 features, Government wants to eliminate passwords, and more.

The truth about the Android Skype vulnerability, new Windows 8 features, Government wants to eliminate passwords, and more.

Actress Kristen Bell says she hopes the latest IMAX documentary Pandas" helps inspire the next generation of biologists and conservationists. "

Description: Pandas is one of Python’s most powerful libraries for data analysis and manipulation, making it a must-learn tool for anyone working with structured data. This video provides a complete introduction to Pandas, from the basics to advanced techniques, along with real-world applications. What You’ll Learn: Introduction to Pandas and its role in data science Understanding Pandas data structures: Series and DataFrames Importing data from CSV, Excel, JSON, and SQL sources Filtering, selecting, and cleaning data with dropna(), fillna(), and interpolate() Sorting, grouping, merging, and joining DataFrames Applying functions with apply() and map() Time series analysis for business and financial data Using Pandas with Matplotlib and Seaborn for data visualization About the Program: The Postgraduate Program in Data Science and Analytics (PGA) by Imarticus Learning is a 6-month, job-assured course designed for graduates and early professionals. It offers: 100% job assurance through 2,000+ hiring partners 300+ learning hours and 25+ hands-on projects Training in 10+ tools including Python, Pandas, Power BI, and Tableau Up to 22.5 LPA highest salary and 52% average salary hike Learn directly from industry experts, work on real datasets, and build the skills needed for a high-growth career in data science and analytics.

Description: Pandas is one of Python’s most powerful libraries for data analysis and manipulation, making it a must-learn tool for anyone working with structured data. This video provides a complete introduction to Pandas, from the basics to advanced techniques, along with real-world applications. What You’ll Learn: Introduction to Pandas and its role in data science Understanding Pandas data structures: Series and DataFrames Importing data from CSV, Excel, JSON, and SQL sources Filtering, selecting, and cleaning data with dropna(), fillna(), and interpolate() Sorting, grouping, merging, and joining DataFrames Applying functions with apply() and map() Time series analysis for business and financial data Using Pandas with Matplotlib and Seaborn for data visualization About the Program: The Postgraduate Program in Data Science and Analytics (PGA) by Imarticus Learning is a 6-month, job-assured course designed for graduates and early professionals. It offers: 100% job assurance through 2,000+ hiring partners 300+ learning hours and 25+ hands-on projects Training in 10+ tools including Python, Pandas, Power BI, and Tableau Up to 22.5 LPA highest salary and 52% average salary hike Learn directly from industry experts, work on real datasets, and build the skills needed for a high-growth career in data science and analytics.

Description: Pandas is one of Python’s most powerful libraries for data analysis and manipulation, making it a must-learn tool for anyone working with structured data. This video provides a complete introduction to Pandas, from the basics to advanced techniques, along with real-world applications. What You’ll Learn: Introduction to Pandas and its role in data science Understanding Pandas data structures: Series and DataFrames Importing data from CSV, Excel, JSON, and SQL sources Filtering, selecting, and cleaning data with dropna(), fillna(), and interpolate() Sorting, grouping, merging, and joining DataFrames Applying functions with apply() and map() Time series analysis for business and financial data Using Pandas with Matplotlib and Seaborn for data visualization About the Program: The Postgraduate Program in Data Science and Analytics (PGA) by Imarticus Learning is a 6-month, job-assured course designed for graduates and early professionals. It offers: 100% job assurance through 2,000+ hiring partners 300+ learning hours and 25+ hands-on projects Training in 10+ tools including Python, Pandas, Power BI, and Tableau Up to 22.5 LPA highest salary and 52% average salary hike Learn directly from industry experts, work on real datasets, and build the skills needed for a high-growth career in data science and analytics.

Pandas-LaTrobe dumped with WikiTeam tools.

As the Nishiyuu Walkers arrived in Ottawa, after having marched over 1600 km from their territories in the James Bay region of Northern Canada, Canada's PM Stephen Harper arrived in Toronto to greet two pandas which had arrived from China destined for the Toronto Zoo. The Nishiyuu Walkers had marched on Ottawa after having been inspired by the Idle No More Movement's uprising against the Harper government's assault in Aboriginal and Treaty Rights. Joshua Blakeney updated Press TV viewers. Interview conduced on March 26, 2013.

Es ist ein diplomatischer Akt der besonderen Art: China wird Ende des Monats zwei Pandabären an den Berliner Zoo übergeben. Die Tiere werden derzeit auf den Transport vorbereitet. Mehr Informationen zum Thema gibt es auch hier: http://www.tagesschau.de/inland/pandas-berlin-101.html Abonnieren Sie hier unseren Kanal: http://bit.ly/2m7yCUu

Paul McCartney was knighted in 1997, not 1996 as tweeted earlier. The culprit has been fed to these adorable pandas! https://t.co/sCpSNkQqeH Source: https://twitter.com/BBCArchive/status/708293051391676416 Uploader: BBC Archive

Welcome to The Morning News Podcast for Wednesday, December 2nd.We begin with another edition of \"Ask the Doctor\". Dr. Craig Jenne, Infectious Disease Specialist from the University of Calgary, answers COVID questions as submitted by you - the listener.Then we head 'stateside' for a look at the latest in the battle against the Coronavirus. We speak with Reggie Cecchini, Global News Washington Correspondent - who brings us details on the dire situation - with over 270,000 COVID-19 deaths, to date.Next, it was a 'bitter sweet' goodbye. We hear about the journey home to China for our two visiting pandas - Ur Shun and Da Mao. Jamie Dorgan, Director of Animal Care with the Calgary Zoo gives us the details of the complex trip.And finally - Pledge Day 2020 takes place this Friday on 770 CHQR. We learn about another charity partner of the Calgary Children's Foundation today - the 'ConnecTeen' program at the Calgary Distress Centre.

Trash Pandas - Episode 33 - Framing Britney Spears

Trash Pandas - Episode 33 - Framing Britney Spears

Blizzard just announced the newest expansion to World of Warcraft: Mists of Pandaria. Tom Merritt joined Tara Long to chat about the new race, the new class, and much more.

Trash Pandas - Episode 22

Trash Pandas - Episode 34

MCC AS

ULTRASOUNDS HAVE CONFIRMED THAT THE VIRGINIA ZOOS LIONESS, ZOLA, IS EXPECTING CUBS VERY SOON. WAIT A SECOND...THERE THEY ARE FOUR BEAUTIFUL CUBS! BACK IN AUGUST OF 2007, ZOLA GAVE BIRTH TO HER FIRST PAIR OF CUBS. THAT WAS THE FIRST LARGE CARNIVORE BIRTH AT THE ZOO IN 35 YEARS. WHEN THE CUBS ARE BORN, ZOLA AND THE NEW CUBS WILL GET SOME TIME OFF FROM THE EXHIBIT TO BOND AND GROW.

an idyllic day at the zoo turns to terror... When Pandas Strike! (actually it's a polar bear but that dosen't roll off the tounge)

Clean comedy for the whole family. This week we're telling stories about: Dead Tropical Fish, Dead Pandas, A Hungry Dentist, and more. Plus our quiz and Would I Lie To You game!

Clean comedy for the whole family. This week we're telling stories about: Dead Tropical Fish, Dead Pandas, A Hungry Dentist, and more. Plus our quiz and Would I Lie To You game!

128 pages

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My kids LOVE this game, and in my constant search to "bling out" our board games I envisioned an actual trash can as both game storage and gameplay centerpiece. The lid does *not* clip or screw on, it simply sits snugly atop the can. The game die, tokens and instructions all fit inside. The lid has a "finger notch" modeled in to allow easy grabbing of the topmost cards during gameplay. Fun! Of course, the game and its original components are all trademarked by Gamewright.

https://www.youtube.com/channel/UCCxlkYjQvQkeX4iiVXylJIQ

https://www.youtube.com/channel/UCCxlkYjQvQkeX4iiVXylJIQ

Leave the pandas alone Gus 🥺 #slow #panda #minecraft

Joe Hooper https://2016.pygotham.org/talks/322/make-data-cleansing-fun-again-with-pandas This talk will provide a fast-paced introduction to cleansing text data with Python. We'll cover specific Pandas tools as well as strategies for managing the cleansing processes. Topics include: slicing/splitting/joining, transforming text, working with missing values, supplementing with additional sources, and dealing with duplicates.

Joe Hooper https://2016.pygotham.org/talks/322/make-data-cleansing-fun-again-with-pandas This talk will provide a fast-paced introduction to cleansing text data with Python. We'll cover specific Pandas tools as well as strategies for managing the cleansing processes. Topics include: slicing/splitting/joining, transforming text, working with missing values, supplementing with additional sources, and dealing with duplicates.

Joe Hooper https://2016.pygotham.org/talks/322/make-data-cleansing-fun-again-with-pandas This talk will provide a fast-paced introduction to cleansing text data with Python. We'll cover specific Pandas tools as well as strategies for managing the cleansing processes. Topics include: slicing/splitting/joining, transforming text, working with missing values, supplementing with additional sources, and dealing with duplicates.

Joe Hooper https://2016.pygotham.org/talks/322/make-data-cleansing-fun-again-with-pandas This talk will provide a fast-paced introduction to cleansing text data with Python. We'll cover specific Pandas tools as well as strategies for managing the cleansing processes. Topics include: slicing/splitting/joining, transforming text, working with missing values, supplementing with additional sources, and dealing with duplicates.

Joe Hooper https://2016.pygotham.org/talks/322/make-data-cleansing-fun-again-with-pandas This talk will provide a fast-paced introduction to cleansing text data with Python. We'll cover specific Pandas tools as well as strategies for managing the cleansing processes. Topics include: slicing/splitting/joining, transforming text, working with missing values, supplementing with additional sources, and dealing with duplicates.

In order to promote public education and public safety, equal justice for all, a better informed citizenry, the rule of law, world trade and world peace, this legal document is hereby made available on a noncommercial basis, as it is the right of all humans to know and speak the laws that govern them. (For more information: 12 Tables of Code ) Name of Legally Binding Document: NZS BIO ZOOPANIC.ALL: Importing Zoo red pandas from all countries (Updated 14 October 1998) Name of Standards Organization: Biosecurity New Zealand LEGALLY BINDING DOCUMENT The Biosecurity Act of 1993

python course of freecodecamp

python course of freecodecamp

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Bojan Lalic from Serbia, Ebraheem Faisal Malik from Pakistan, and Torbjorn Sassersson from NewsVoice in Sweden comment on their first experiences meeting Chinese pandas in Chengdu, China. The video was produced by Sichuan Newsnet Media Group, Chengdu. Website: http://www.newssc.org/

Bojan Lalic from Serbia, Ebraheem Faisal Malik from Pakistan, and Torbjorn Sassersson from NewsVoice in Sweden comment on their first experiences meeting Chinese pandas in Chengdu, China. The video was produced by Sichuan Newsnet Media Group, Chengdu. Website: http://www.newssc.org/

Bojan Lalic from Serbia, Ebraheem Faisal Malik from Pakistan, and Torbjorn Sassersson from NewsVoice in Sweden comment on their first experiences meeting Chinese pandas in Chengdu, China. The video was produced by Sichuan Newsnet Media Group, Chengdu. Website: http://www.newssc.org/

Benchmark for different operations in pandas against various dataframe sizes. Fast Pandas A Benchmarked Pandas Cheat Sheet Pandas is one of the most flexible and powerful tools available for data scientists and developers. Being very flexible, one can perform a given task in several ways. This project aims to benchmark the different available methods in such situations; moreover, there is a special section for functions found in both numpy and pandas. Introduction: This project is not intended to only show the obtained results but also to provide others with a simple method for benchmarking different operations and sharing their results. Below is a quick example of how to use the benchmarking class:```pythonfrom Benchmarker import Benchmarkerimport numpy as np def pandas_sum(df): return df["A"].sum() def numpy_sum(df): return np.sum(df["A"]) params = { "df generator": 'pd.DataFrame(np.random.randint(1, df size, (df size, 2)), columns=list("AB"))', "functions to evaluate": [pandas sum, numpy sum], "title": "Pandas Sum vs Numpy Sum",}benchmark = Benchmarker( params)benchmark.benchmark all()benchmark.print results()benchmark.plot results()```The first parameter passed to the class constructor is df_generator which is simply a function that generates a random dataframe. This function has to be define in terms of df_size ** so that different dataframes with increasing sizes are generated. The second parameter is the list of functions to be evaluated, and the last one is the title of the resulting plot. Calling plot_results( ) will show and save a plot like the one shown below containing two subplots:* The first subplot shows the average time it has taken each function to run against different dataframe sizes. Note that this is a semilog plot, i.e. the y-axis is shown in log scale. * The second subplot shows how other functions performed with respect to the first function. You can clearly see that pandas sum is slightly faster than numpy sum, for dataframes below one million rows. Results Summary: 1 - Pandas benchmark. 1.1 Dropping duplicate rows: There are severals methods for dropping duplicate rows in pandas, three of which are tested below: ```pythondef duplicated(df): return df[~df["A"].duplicated(keep="first")].reset_index(drop=True) def drop duplicates(df): return df.drop duplicates(subset="A", keep="first").reset_index(drop=True) def group by drop(df): return df.groupby(df["A"], as_index=False, sort=False).first() ```* duplicated is the fastest; irrespective of size. * The group_by drop shows an interesting trend. It could be possible for it to be faster than duplicated for data frames larger than 100 million rows. 1.2 - Iterating over all rows: Tested functions: ```pythondef iterrows_function(df): for index, row in df.iterrows(): pass def itertuples_function(df): for row in df.itertuples(): pass``` itertuples is significantly faster than iterrows (up to 50 times faster) 1.3 - Selecting rows: Tested functions: ```pythondef bracket_selection(df): return df[(df["A"] > 0) & (df["A"] < 100)] def query_selection(df): return df.query("A > 0 and A < 100") def loc_selection(df): return df.loc[(df["A"] > 0) & (df["A"] < 100)] def ne_selection(df): A = df["A"].values return df[ne.evaluate("(A > 0) & (A < 100)")] def ne create selection(df): A = df["A"].values mask = ne.evaluate("(A > 0) & (A < 100)") return pd.DataFrame(df.values[mask], df.index[mask], df.columns)``` * ne_create_selection is the fastest method for dataframes smaller than 10000 rows, followed bt ne_selection for larger data frames. * loc and query selections are identical in performance. * Square bracket selection is the slowest method. 1.4 - Creating a new column: Tested functions: ```pythondef regular(df): df["E"] = df["A"] * df["B"] + df["C"] def eval method(df): df.eval("E = A * B + C", inplace=True)``` * The regular method is faster than the eval method. * eval methods shows and interesting erratic behavior that I couldn't explain; however, I repeated the test several times with different mathematical operations and still reproduced the same plot every time. 2 - Pandas vs Numpy. This section tests the performance of functions that are found in both numpy and padnas. 2.1 - Summation performance: Tested functions: ```pythondef pandas_sum(df): return df["A"].sum() def numpy_sum(df): return np.sum(df["A"])``` pandas sum is slightly faster than numpy sum, for dataframes below one million rows. 2.2 - Sort performance: Tested functions: ```pythondef pandas sort(df): return df["A"].sort values() def numpy_sort(df): return np.sort(df["A"])``` numpy_sort is considerably faster than pandas, irrespective of size; although they both use quicksort as the default sorting algorithm. 2.3 - Unique performance: Tested functions: ```pythondef pandas_unique(df): return df["A"].unique() def numpy_unique(df): return np.unique(df["A"])``` For data frames over 100 rows pandas unique is faster than numpy. It is worth noting that unlike pandas unique, numpy unique returns a sorted array, which explains the discrepancy in results 2.4 - Median performance: Tested functions: ```pythondef pandas_median(df): return df["A"].median() def numpy_median(df): return np.median(df["A"])``` No significant statistical difference in performance. 2.5 - Mean performance: Tested functions: ```pythondef pandas_mean(df): return df["A"].mean() def numpy_mean(df): return np.mean(df["A"])``` pandas mean is slightly faster than numpy mean, for dataframes below one million rows. 2.6 - Product performance: Tested functions: ```pythondef pandas_prod(df): return df["A"].prod() def numpy_prod(df): return np.prod(df["A"])``` pandas product is slightly faster than numpy product, for dataframes below one million rows. Extra notes: Extra parameters: The class constructor has three other optional parameters: "user_df_size_powers": List[int] containing the log10(sizes) of the test_dfs"user_loop_size_powers": List[int] containing the log10(sizes) of the loops_sizes"largest_df_single_test" (defualt = True) You can pass custom sizes for the dataframes and loops used in benchmarking, this is suggested when there seems to be noise in th results; i.e. you are unable to maintain consistency over different runs. The third parameter, largest_df_single_test , is set to true by default; since the last dataframe has 100 million rows and for some operations it will take a large amount of time to complete a single task. Warnings: The benchmarker will warn you if the results returned by the evaluated functions are not identical. You might not need to worry about that, as it has been shown in the benchmarking of the np.unique function above. Future work: -Using median instead of mean as it is less prone to noise. -Benchmarking memory consumption. Got something on your mind you would like to benchmark ? We are waiting for your results. To restore the repository download the bundle wget https://archive.org/download/github.com-mm-mansour-Fast-Pandas_-_2018-03-21_03-16-33/mm-mansour-Fast-Pandas_-_2018-03-21_03-16-33.bundle and run: git clone mm-mansour-Fast-Pandas_-_2018-03-21_03-16-33.bundle Source: https://github.com/mm-mansour/Fast-Pandas Uploader: mm-mansour Upload date: 2018-03-21

Tutorials on how to use pandas effectively to do data analysis Learn Pandas A repository for learning how to do data analysis with Pandas. All material will be in Jupyter notebooks containing exercises with detailed solutions. To restore the repository download the bundle wget https://archive.org/download/github.com-tdpetrou-Learn-Pandas_-_2017-12-08_21-25-37/tdpetrou-Learn-Pandas_-_2017-12-08_21-25-37.bundle and run: git clone tdpetrou-Learn-Pandas_-_2017-12-08_21-25-37.bundle Source: https://github.com/tdpetrou/Learn-Pandas Uploader: tdpetrou Upload date: 2017-12-08