Ninama

1. Tape Reading 101 Course Tiger Balm
2. Label Reading 101
3. Spark Plug Reading 101
4. Tape Reading 101 Course Tiger Game
5. Tape Reading 101 Course Tigers

1. Tiger Bucks RIT is alive with energy and excitement. It won’t take long for you to find your niche in this community because there are so many ways to be involved.
2. Day trading with advanced tape reading tactics. Watch active trader Ken Calhoun, President of Daytrading University as he reveals top trading tips in this real trade walkthrough using time.
3. Tape Reading 101 Course Downloads. Align and tack with tape then flip over and run one long piece of tape along the seam. I made my chart a whopping 7 The place for everything in Oprah's world. Market Maker Method – 4 Day Course with Indicators. Rife 101 Energy Frequency Machine true testimonials for cancer, Lyme, arthritis.

Buy quality land surveying equipment with Tiger Supplies to attain accuracy in the survey and outstanding results. Browse our website to explore our incomparable GPS survey tools.

Pandas is one of the most popular Python libraries for Data Science and Analytics. I like to say it’s the “SQL of Python.” Why? Because pandas helps you to manage two-dimensional data tables in Python. Of course, it has many more features. In this pandas tutorial series, I’ll show you the most important (that is, the most often used) things that you have to know as an Analyst or a Data Scientist. This is the first episode and we will start from the basics!

Note 1: this is a hands-on tutorial, so I recommend doing the coding part with me!

Before we start

If you haven’t done so yet, I recommend going through these articles first:

To follow this pandas tutorial…

1. You will need a fully functioning data server with Python3, numpy and pandas on it.
   Note 1 : Again, with this tutorial you can set up your data server and Python3. And with this article you can set up numpy and pandas, too.
   Note 2: or take this step-by-step data server set up video course.
2. Next step: log in to your server and fire up Jupyter. Then open a new Jupyter Notebook in your favorite browser. (If you don’t know how to do that, I really do recommend going through the articles I linked in the “Before we start” section.)
   Note: I’ll also rename my Jupyter Notebook to “pandas_tutorial_1”.
3. Import numpy and pandas to your Jupyter Notebook by running these two lines in a cell:

   Note: It’s conventional to refer to ‘pandas’ as ‘pd’. When you add the as pd at the end of your import statement, your Jupyter Notebook understands that from this point on every time you type pd, you are actually referring to the pandas library.

Okay, now we have everything! Let’s start with this pandas tutorial!
The first question is:

How to open data files in pandas

You might have your data in .csv files or SQL tables. Maybe Excel files. Or .tsv files. Or something else. But the goal is the same in all cases. If you want to analyze that data using pandas, the first step will be to read it into a data structure that’s compatible with pandas.

Pandas data structures

There are two types of data structures in pandas: Series and DataFrames.

Series: a pandas Series is a one dimensional data structure (“a one dimensional ndarray”) that can store values — and for every value it holds a unique index, too.

Pandas Series example

DataFrame: a pandas DataFrame is a two (or more) dimensional data structure – basically a table with rows and columns. The columns have names and the rows have indexes.

In this pandas tutorial, I’ll focus mostly on DataFrames. The reason is simple: most of the analytical methods I will talk about will make more sense in a 2D datatable than in a 1D array.

Loading a .csv file into a pandas DataFrame

Okay, time to put things into practice! Let’s load a .csv data file into pandas!
There is a function for it, called read_csv().

Start with a simple demo data set, called zoo! This time – for the sake of practicing – you will create a .csv file for yourself! Here’s the raw data:

Go back to your Jupyter Home tab and create a new text file…

…then copy-paste the above zoo data into this text file…

… and then rename this text file to zoo.csv!

Okay, this is our .csv file.
Now, go back to your Jupyter Notebook (that I named ‘pandas_tutorial_1’) and open this freshly created .csv file in it!

Again, the function that you have to use is: read_csv()
Type this to a new cell:

pd.read_csv('zoo.csv', delimiter = ',')

And there you go! This is the zoo.csv data file, brought to pandas. This nice 2D table? Well, this is a pandas dataframe. The numbers on the left are the indexes. And the column names on the top are picked up from the first row of our zoo.csv file.

To be honest, though, you will probably never create a .csv data file for yourself, like we just did… you will use pre-existing data files. So you have to learn how to download .csv files to your server!

If you are here from the Junior Data Scientist’s First Month video course then you have already dealt with downloading your .txt or .csv data files to your data server, so you must be pretty proficient in it… But if you are not here from the course (or if you want to learn another way to download a .csv file to your server and to get another exciting dataset), follow these steps:

I’ve uploaded a small sample dataset here: DATASET

(Link: 46.101.230.157/dilan/pandas_tutorial_read.csv)

If you click the link, the data file will be downloaded to your computer. But you don’t want to download this data file to your computer, right? You want to download it to your server and then load it to your Jupyter Notebook. It only takes two steps.

STEP 1) Go back to your Jupyter Notebook and type this command:

!wget 46.101.230.157/dilan/pandas_tutorial_read.csv

This downloaded the pandas_tutorial_read.csv file to your server. Just check it out:

See? It’s there.

If you click it…

…you can even check out the data in it.

STEP 2) Now, go back again to your Jupyter Notebook and use the same read_csv function that we have used before (but don’t forget to change the file name and the delimiter value):

pd.read_csv('pandas_tutorial_read.csv', delimiter=';')

The data is loaded into pandas!

Does something feel off? Yes, this time we didn’t have a header in our csv file, so we have to set it up manually! Add the names parameter to your function!

pd.read_csv('pandas_tutorial_read.csv', delimiter=';', names = ['my_datetime', 'event', 'country', 'user_id', 'source', 'topic'])

Better!
And with that, we finally loaded our .csv data into a pandas dataframe!

Note 1: Just so you know, there is an alternative method. (I don’t prefer it though.) You can load the .csv data using the URL directly. In this case the data won’t be downloaded to your data server.

read the .csv directly from the server (using its URL)

Note 2: If you are wondering what’s in this data set – this is the data log of a travel blog. This is a log of one day only (if you are a JDS course participant, you will get much more of this data set on the last week of the course ;-)). I guess the names of the columns are fairly self-explanatory.

Selecting data from a dataframe in pandas

This is the first episode of this pandas tutorial series, so let’s start with a few very basic data selection methods – and in the next episodes we will go deeper!

1) Print the whole dataframe

The most basic method is to print your whole data frame to your screen. Of course, you don’t have to run the pd.read_csv() function again and again and again. Just store its output the first time you run it!

article_read = pd.read_csv('pandas_tutorial_read.csv', delimiter=';', names = ['my_datetime', 'event', 'country', 'user_id', 'source', 'topic'])

After that, you can call this article_read value anytime to print your DataFrame!

2) Print a sample of your dataframe

Sometimes, it’s handy not to print the whole dataframe and flood your screen with data. When a few lines is enough, you can print only the first 5 lines – by typing:

article_read.head()

Or the last few lines by typing:

article_read.tail()

Or a few random lines by typing:

article_read.sample(5)

3) Select specific columns of your dataframe

This one is a bit tricky! Let’s say you want to print the ‘country’ and the ‘user_id’ columns only.
You should use this syntax:

article_read[['country', 'user_id']]

Any guesses why we have to use double bracket frames? It seems a bit over-complicated, I admit, but maybe this will help you remember: the outer bracket frames tell pandas that you want to select columns, and the inner brackets are for the list (remember? Python lists go between bracket frames) of the column names.

By the way, if you change the order of the column names, the order of the returned columns will change, too:

article_read[['user_id', 'country']]

This is the DataFrame of your selected columns.

Note: Sometimes (especially in predictive analytics projects), you want to get Series objects instead of DataFrames. You can get a Series using any of these two syntaxes (and selecting only one column):

article_read.user_id
article_read['user_id']

output is a Series object and not a DataFrame object

4) Filter for specific values in your dataframe

If the previous one was a bit tricky, this one will be really tricky!

Let’s say, you want to see a list of only the users who came from the ‘SEO’ source. In this case you have to filter for the ‘SEO’ value in the ‘source’ column:

article_read[article_read.source 'SEO']

It’s worth it to understand how pandas thinks about data filtering:

STEP 1) First, between the bracket frames it evaluates every line: is the article_read.source column’s value 'SEO' or not? The results are boolean values (True or False).

STEP 2) Then from the article_read table, it prints every row where this value is True and doesn’t print any row where it’s False.

Does it look over-complicated? Maybe. But this is the way it is, so let’s just learn it because you will use this a lot! 😉

Functions can be used after each other

It’s very important to understand that pandas’s logic is very linear (compared to SQL, for instance). So if you apply a function, you can always apply another one on it. In this case, the input of the latter function will always be the output of the previous function.

E.g. combine these two selection methods:

article_read.head()[['country', 'user_id']]

This line first selects the first 5 rows of our data set. And then it takes only the ‘country’ and the ‘user_id’ columns.

Could you get the same result with a different chain of functions? Of course you can:

article_read[['country', 'user_id']].head()

In this version, you select the columns first, then take the first five rows. The result is the same – the order of the functions (and the execution) is different.

One more thing. What happens if you replace the ‘article_read’ value with the original read_csv() function:

pd.read_csv('pandas_tutorial_read.csv', delimiter=';', names = ['my_datetime', 'event', 'country', 'user_id', 'source', 'topic'])[['country', 'user_id']].head()

This will work, too – only it’s ugly (and inefficient). But it’s really important that you understand that working with pandas is nothing but applying the right functions and methods, one by one.

Test yourself!

As always, here’s a short assignment to test yourself! Solve it, so the content of this article can sink in better!

Select the user_id, the country and the topic columns for the users who are from country_2! Print the first five rows only!

Okay, go ahead and solve it!

.
.
.

Tape Reading 101 Course Tiger Balm

And here’s my solution!
It can be a one-liner:

article_read[article_read.country 'country_2'][['user_id','topic', 'country']].head()

Or, to be more transparent, you can break this into more lines:

Either way, the logic is the same. First you take your original dataframe (article_read), then you filter for the rows where the country value is country_2 ([article_read.country 'country_2']), then you take the three columns that were required ([['user_id','topic', 'country']]) and eventually you take the first five rows only (.head()).

Conclusion

You are done with the first episode of my pandas tutorial series! Great job! In the next article, you can learn more about the different aggregation methods (e.g. sum, mean, max, min) and about grouping (so basically about segmentation). Stay with me: Pandas Tutorial, Episode 2!

• If you want to learn more about how to become a data scientist, take my 50-minute video course: How to Become a Data Scientist. (It’s free!)
• Also check out my 6-week online course: The Junior Data Scientist’s First Month video course.

Cheers,
Tomi Mester

Yes, there is a United States Bocce Federation and that is where you can find the definitive answer to any question you have about the traditional Italian lawn bowling game. For example, “Question: Isn’t bocce a game for old people?” Answer: No!

Formerly the pastime of old men in city parks, bocce has enjoyed a renaissance in recent years as people realize its value as gentle backyard entertainment. All you need to play (and this comes straight from the bocce federation people) is two teams, nine balls, and a tape measure to settle disputes. Plus, of course, a proper playing surface.

Do you have room to build a bocce court of your own? Official dimensions of a court are 86.92 feet long by 13.12 feet wide. Unofficially, you can make a smaller bocce court depending on the size of your space. Here’s everything you need to know about bocce courts:

Above: In northern California on a steep cliff above Stinson Beach, Scott Lewis Landscape Architecture sited a bocce court beneath date palms. Photograph courtesy of Scott Lewis Landscape Architecture.

DIY bocce court versus a professional installation:

Landscape designers, who say a bocce court is a popular item on clients’ wish lists these days, can create a permanent hardscape feature, a custom court to fit your space with extras such as built-in seating, retaining walls, and landscape plants. Or if you are handy and like projects that require manual labor, you can make your own court.

Above: Remodelista Architect and Design Directory member Arterra Landscape Architects took advantage of hillside views when siting a bocce court in a California landscape. Photograph courtesy of Arterra.

How do you make a bocce court surface?

Your court needs a flat surface to prevent balls from careening off in odd directions. After you choose a site for the court (make sure it’s a long, skinny rectangle shape, even if you don’t have space for an official-size court), level it. You can use a laser level such as a PLS Laser Level ($269 from Amazon). Grade the soil by using a Shovel and a Wheelbarrow to haul loads.

Above: Landscape architect Pete Pedersen and his son built a half-size bocce court in their backyard. “It’s like playing adult marbles,” says Pedersen. For more of his garden, see Garden Secrets: What a Landscape Architect Plants at Home. Photograph courtesy of Pedersen Associates.

How do you make a frame for a bocce court?

For a DIY bocce court, use lumber to make a low-sided enclosure to keep the balls inside the court during play. The height of the frame should be from 8 to 12 inches.

Above: Greg and Rainy Smith, who live in Manhattan Beach, California, removed their lawn to make room for family social gatherings. With string lights, built-in seating, and a bocce court, this is the ultimate outdoor party room. Nobody misses the lawn (or the higher monthly water bill). For more, see Gardenista Considered Design Awards 2015: The Best Outdoor Living Space. Photograph courtesy of Greg and Rainy Smith.

What kind of surface should a bocce court have?

Above: Photograph via Myco Supply, which sells Bulk Crushed Oyster Shells for bocce courts.

You want a court surface that will allow balls to roll fast and smoothly, without bouncing. For a DIY bocce court, first spread a 3-inch layer of drainage rock. Next add a weed barrier (such as a layer of landscape cloth). Then spread a 3-inch layer of crushed rock, fine gravel, or decomposed granite. You can also top dress the surface with a special bocce court blend of crushed oysters; for information, see Myco Supply.

Label Reading 101

What kind of maintenance does a bocce court require?

Above: A 24-Inch Gator Rake is $139 and a Small Hand Roller is $260; both from 10-S.

If you have a layer of crushed oyster shells, you can wet down the surface with a hose from time to time and roll it smooth with a lawn roller. You also can keep the surface even with a drag rake. Keep debris, twigs, pebbles, and other impediments off the surface so balls will roll smoothly.

Spark Plug Reading 101

What about bocce balls?

Above: A set of eight wooden Bocce balls plus a jack is $320 from Fredericks & Mae.

For tournament play, bocce balls should be 107 millimeters in diameter (thank you, bocce federation) and weigh 920 grams (about 2 pounds). For a set of bocce balls meeting those standards, consider the St. Pierre Tournament Bocce Set ($103.99 from Amazon).

For the official rules of the game, see How to Play Bocce Ball.

Other accessories?

Tape Reading 101 Course Tiger Game

Above: Settle disputes with a tape measure. A 20-Meter Field Tape is $48 from Guideboat.

For more of our favorite lawn games, see:

Tape Reading 101 Course Tigers

• 10 Favorites: Stylish Lawn Games.
• Bye, Bye Birdie.
• Lawn Games: Petanque from Provence.