Shape 0 python что это

Shape 0 python что это

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas pd.size, .shape, and .ndim are used to return the size, shape, and dimensions of data frames and series.

Dataset Used

To download the data set used in the following example, click here. In the following examples, the data frame used contains data from some NBA players. The image of the data frame before any operations are attached below.

Pandas DataFrame size()

The size property is used to get an int representing the number of elements in this object and Return the number of rows if Series. Otherwise, return the number of rows times the number of columns if DataFrame.

The Pandas Shape Attribute – A Complete Guide

Pandas is an extensive library for external data preprocessing and internal dataset creation. It is one of the main packages that help in preprocessing information and cleaning it for better use.

The best feature is that it enables to read and fetch a large amount of data from the servers.

This helps a lot better in Python’s web scraping and collection of critical points online. This article speaks about one of the notable features of this module which is The Panda’s Shape Attribute.

Prerequisites

Before we start the main thing is we need to check out tools and weapons for this game. So, let us make sure of it.

Tools and technologies:

1. Python: version 3.6 or above
2. IDE: Jupyter Notebooks
3. Browser: Google Chrome
4. Environment: Anaconda
5. Supportive packages: Numpy and Matplotlib
6. A stable internet connection (necessary only to read data from the server).

Also we will make sure what are we going to cover in this article:

What we’ll cover in this article:

1. What is the shape attribute in Pandas
2. Reading a dataset
3. Using shape in that dataset

Now we are ready for this action so let us jump right in!

What is the shape attribute in Pandas?

General Format Of Table 1

A data frame is the actual representation of information about a specific topic. This can be from various data streams and industry sections. Probably every individual and organization from particular sectors in this world of modernization maintains critical data. Its principal or major format is Tabular. But this tabular data is in various extensions like SQL, Excel, JSON, etc. The below image shows the actual picture:

It can be either small or large. In most cases, the datasheet is very larger than we expect. Thus, some human mistakes may happen while taking into the record the count of rows and columns.

So, to tackle this difficulty, the shape attribute in the pandas library is for checking the actual number of rows and columns inside a dataset or a data frame.

Syntax to read any dataset’s shape – This is the general syntax to read the shape of the dataset:

Reading a dataset in Pandas

The dataset reading is bringing into the picture what actually exists inside it. This is performed using the read function in Pandas. It has different forms for different file extensions. We will read three datasets to check each one’s shape.

Datasets used:

1. Test_set.csv
2. salary.csv
3. titanic.csv

General syntax to read a dataset:

Dataset 1

Reading dataset 1 and retrieving its shape

In the above image, we can see how the shape attribute works. It returns a tuple that has two values. Remember that the first value denotes the number of rows and the second value denotes the number of columns. In short, this tells us that the dataset is much larger. It has 2,671 rows and 10 columns.

Dataset 2

Reading dataset 2 and retrieving its shape

Its name is salary.csv this dataset shape is (16, 4). Thus it has 16 rows and 4 columns.

Dataset 3

Reading dataset 3

This dataset is titanic.csv. From the shape attribute, we can see that it has 418 rows and 12 columns present in this dataset.

Some different ways to use the shape attribute

Now that we came to know how to use shape through these three examples. There are some notable key points that we can make use of for this attribute.

1. To retrieve only row count.
2. To retrieve only column count.

As we know that it returns a tuple of rows, columns. So, we can use index slicing for this. tuples are immutable but, the elements are accessible through indexing methods. It is the same as we do with the lists. Let us see with a codebase example:

To retrieve row count access the zeroth index and for the column count access the first index

Conclusion

This is how the shape attribute performs in Pandas. It is a very important and one of the key functions that we use for the data preprocessing.

Python shape() method — All you need to know!

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Hello, readers! This article talks about the Python shape() method and its variants in programming with examples.

So, let us begin!!

Use of Python shape() method

When it comes to the analysis of data and its variants, it is extremely important to realize the volume of data. That is, before we plan to analyze the data and perform synthesis on it, we need to be aware of the dimensions of the data.

This is when the Python shape() method comes into the picture.

With the shape() method, comes the flexibility to obtain the dimensions of any Python object. Yes, it returns a tuple value that indicates the dimensions of a Python object.

To understand the output, the tuple returned by the shape() method is the actual number of elements that represent the value of the dimension of the object.

Usually, on a broader scale, the shape() method is used to fetch the dimensions of Pandas and NumPy type objects in python.

Every value represented by the tuple corresponds to the actual dimension in terms of array or row/columns.

Let us now have a look at the variant of the same in the upcoming section.

Variant 1: Pandas shape attribute

When we try to associate the Pandas type object with the shape method looking for the dimensions, it returns a tuple that represents rows and columns as the value of dimensions.

Syntax:

We usually associate shape as an attribute with the Pandas dataframe to get the dimensions of the same.

Example 01:

In this example, we have created a dataframe from a Python list using DataFrame() method. Post which, we apply the dataframe.shape to check for the dimensions.

As the data that we have passed has two rows and two columns (2×2), the shape method returns us the number of rows and columns as the result.

Output:

Example 02:

In this example, we have created an empty dataframe using DataFrame() function. Then, with the shape() method, we can get the dimensions of the empty dataframe

Output:

Variant 2: NumPy shape method

With NumPy data structure, we store data elements in the form of an array. When we associate the shape() method with the NumPy array, the dimensions of the array are represented in the form of a tuple.

Syntax:

Example 01:

Here, we have created a NumPy array with no dimensions. Further, we have applied the shape() method on the array to get the dimensions of the created array.

Output:

Example 02:

In this example, we have created a NumPy array and added elements to it. This is achieved using numpy.array() function. Now, we apply the shape() method to the array of elements.

Output:

Conclusion

By this, we have come to the end of this topic. Feel free to comment below, in case you come across any questions.

For more such posts related to Kubernetes, Stay tuned with us.

Till then, Happy Learning! 🙂

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Получение формы или размера в DataFrame Pandas

Чтобы получить форму Pandas DataFrame, используйте DataFrame.shape. Свойство shape возвращает кортеж, представляющий размерность DataFrame, формат формы будет (строки, столбцы).

В этом руководстве мы узнаем, как получить форму, другими словами, количество строк и количество столбцов в DataFrame, с помощью примеров.

Пример

В следующем примере мы найдем форму DataFrame. Кроме того, вы можете получить количество строк или столбцов, используя индекс формы.

Пример 2

В этом примере мы инициализируем пустой DataFrame и попытаемся найти его форму. Конечно, DataFrame.shape вернет (0, 0).

Количество строк и столбцов равно нулю.

В этом руководстве на примерах Python мы узнали, как найти форму измерения DataFrame, другими словами, количество строк и столбцов.