3 Data Display

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From the course by Nanjing University

Data Processing Using Python

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Nanjing University

Data Processing Using Python

107 ratings

This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields. The course has been updated. Updates in the new version are : 1) the whole course has moved from Python 2.x to Python 3.x 2) Added manual webpage fetching and parsing. Web API is also added. 3) Improve the content order and enrich details of some content especially for some practice projects. Note: videos are in Chinese (Simplified) with English subtitles. All other materials are in English.

From the lesson

Python Data Statistics and Visualization

Welcome to learn Module 04 “Python Data Statistics and Visualization”! In this module, I will show you, over the entire process of data processing, the unique advantages of Python in data processing and analysis, and use many cases familiar to and loved by us to learn about and master methods and characteristics. After learning this module, you can fast and effectively mine your desired or expected or unknown results from a large amount of data, and can also present those data in various images. In addition, the data statistics modes of all third party packages in Python are extraordinarily and surprisingly strong, but we, as average persons, can still understand and possess them.

1 Convenient and Fast Data Acquisition12:56

2 Data Preparations6:44

3 Data Display4:37

4 Data Selection8:56

5 Simple Statistics and Processing8:54

Meet the Instructors

ZHANG Li
associate professor
Department of Computer Science

After data cleansing is well done,

during subsequent processing or when viewing final results,

we often need to browse data.

Sometimes, the result of a program may be found to be incorrect.

At this time, you might get stubborn with the program,

feeling overwhelmed with sorrow and unable to fall asleep.

However, mistakes might have existed in data,

actually during your previous data processing.

Even worse, mistakes might exist

even when you input

the initial data

or imported them.

Thus, to avoid such tragedies,

let's waste no time in seeing

what data display can do in Python.

After previous processing,

as we see, the data have already got a very clear structure

with its own columns

and its own index.

The meanings of these data

are also very clear.

Then, why do we need to display data?

It is for better and clearly viewing

the data structure and the contents for selection.

We generally display such things,

like, view the row index,

view the column index

or view the value of data,

view the description of data, etc.

As for the row index, we all know the "index" attribute.

And, "columns", for the column index.

To view the value, use "values".

To view the basic description of data,

we use "describe".

The link of data display enables us to better view

whether there's any problem in previous data cleansing.

For example, in such a data frame of our "djidf",

as we know, it has a column "lasttrade".

Its current format is the "float" type.

Previously, at the website,

its format, as we acquired, was a string.

We processed it with a program,

and converted such data into a "float" type,

into a list.

Then, use the list to generate a DataFrame.

After processing with such a method,

the data become the "float" type.

This would make subsequent processing

like calculation and sorting

more conveniently and efficiently.

For this reason, data display happens to be a re-check process.

Besides, we can view more data details,

say, to view the basic information of the first 5 and last 5 stocks

of Dow Jones Industrial Average.

It's indeed the row display in DataFrame.

We may use the original traditional slicing method.

Sure, this dedicated method is also OK.

As we see,

here are two methods.

We can understand their literal meanings. So easy.

To add an argument 5 in "head()" means to view the first 5.

To add an argument 5 in "tail()" means to view the last 5.

Sure, we may still use the original traditional slicing method.

Apart from row display,

it's also possible to conduct column display.

Give it a go.

With the traditional slicing method,

we select the first 5 records in this DataFrame of "djidf".

Sure we may also use its head method to select the first 5 records.

If we're to select one row in it,

say, lasttrade,

we may write it like this.

Besides, we may view,

say, its dimensions and its element quantity etc.

If data display is correct,

generally, we select them or part of them for use.

In next section, we'll focus on dedicated selections.

We generally conduct some basic display operations and then

conduct further selection operations.

Selections also simultaneously include display.

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