Python pandas

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Data frames:

Explore Python's data analysis. Pandas data facilitates data retrieval or changes the index selection and redirection of your data.

Pandas is popular Python kits on scientific data, and good reason: Provides powerful data structures, visual and flexible data processing and simple data processing, and much more. Data from one of these buildings.
This training covers the spread of Pandas data, from the basic activities of development activities, to address 11 most commonly asked questions - and avoid the fears of Pythonians already experiencing.

What is pandas information?

Before we start, briefly summarize how data is collected.
Those who know the language know the data of the device to be a way of saving information on the sophisticated methods that can not be easily monitored. Each line of tickets depends on the size or size of the model, while each reference is a vector which contains the specific data changes. This means that the queue, which does not need to be included, but may include the same value: it can be number, character, logic, and other
Now, data from Python is very similar: they come to the library, defining a two-dimensional framework for marking the various types of competencies.
Generally, it can be said that the panda's data structure consists of three main components: data, indexes, and columns.
1. First, the data structure may contain information:
• Panda data system
• Pandas series: A single unit that is labeled with the ability to have all types of data with the appliance or cables. Examples of a series of items in a series of data.
• Accident and accident, which can be designed or documented
• Two-way system
• Glossary of one-sided dictionary and contacts, lists, dictionaries or series.
           # A structured array
           my_array = np.ones(3, dtype=([('foo', int), ('bar', float)]))
           # Print the structured array
           (my_array['foo'])
           # A record array
           my_array2 = my_array.view(np.recarray)
          # Print the record array
          (my_array2.foo)
 Note the difference between the stage (np.ndarray). Sort the types of real data, while the second is the process of designing other data structures.
Structured devices allow the user to improve the domain name information: the following example, different formats for the three copies. The first part of each tuple is called for and will be the type of int, while the second is called a bar and a foil.
Take the pictures, on the other hand, to expand the shape of the test. They allow users to enter the standardized test scores on the subject. Next time, it is considered that the interlocutors have access to R2 records.
In addition to the data, you can define the name of the index and the data structure in your thigh. index, one side, indicating the difference between travel, while the tactic name reflects the difference. You will see later that the two sections of this data will be very useful if you are dealing with your data.

How to create pandas data:

It's obvious, so the data carries the first steps that are almost anything you want to do when it comes to the single data in Python. Sometimes, you should start scratching, but you can also change the format of other data, such as a checklist or test pandas, but pandas data gap. In this section, it is possible only to cover. However, if you want to read more when making detailed information, you can complete the background information.
                data = np.array([['','Col1','Col2'],
                ['Row1',1,2],
                ['Row2',3,4]])
                print(pd.DataFrame(data=data[1:,1:],
                index=data[1:,0],
                columns=data[0,1:]))
Many things can include contributing to the 'data item', the number is Numerous. Create the database box in a series of leaflets, but you can forward the data () to the data collection techniques.
Be sure to check how the code is upgraded in the selected sections of the NumPy Issues section on the data structure of you: First select the value in the list from ROW1 at the beginning2 queue, then select the diagram or ROW1 number Row2 after the name the name of Col1 and col2.
              # Take a 2D array as input to your DataFrame 
              my_2darray = np.array([[1, 2, 3], [4, 5, 6]])
              print(________________)
              # Take a dictionary as input to your DataFrame 
              my_dict = {1: ['1', '3'], 2: ['1', '2'], 3: ['2', '4']}
              print(________________)
              # Take a DataFrame as input to your DataFrame 
              my_df = pd.DataFrame(data=[4,5,6,7], index=range(0,4), columns=['A'])
               print(________________)
             # Take a Series as input to your DataFrame
  my_series = pd.Series({"Belgium":"Brussels", "India":"New Delhi", "United Kingdom":"London", "United States":"Washington"})
               print(________________)

In addition, you can also see that, in the above-mentioned DataCamp, you have published a small selection of data. The same way as the 2D distribution: For example, the first line shows you want to look at the data so that, then, the pillar. Do not forget that the icons start 0! For the information in the previous example, go and look at lines 1 to the end and select all of the following points behind the topic 1. The result, options 1, 2, 3 and 4 are completed.

Fundamental DataFrame Operations:

Now that you have put your data in a more convenient Pandas DataFrame structure, it’s time to get to the real work!
This first section will guide you through the first steps of working with DataFrames in Python. It will cover the basic operations that you can do on your newly created DataFrame: adding, selecting, deleting, renaming, … You name it!

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