Json to png python


  • Python JSON
  • A cheat sheet for working with JSON Data in Python
  • ESP32 MicroPython: Parsing JSON
  • How can i convert json file from labelme interface to png or image format file?
  • How to use Python 3 to save an image via a JSON payload to a SAP HANA OData endpoint
  • Python JSON

    In cases like this, a combination of command line tools and Python can make for an efficient way to explore and analyze the data. When data is stored in SQL databases, it tends to follow a rigid structure that looks like a table.

    In the dataset above, each row represents a country, and each column represents some fact about that country. This is called unstructured data. A good example is a list of events from visitors on a website. Each event has different fields, and some of the fields are nested within other fields.

    This type of data is very hard to store in a regular SQL database. JSON is a way to encode data structures like lists and dictionaries to strings that ensures that they are easily readable by machines. Python has great JSON support, with the json library. We can both convert lists and dictionaries to JSON, and convert strings to lists and dictionaries.

    JSON data looks much like a dictionary would in Python, with keys and values stored. You can download the data here.

    The data contains information about where the violation happened, the type of car, demographics on the person receiving the violation, and some other interesting information. There are quite a few questions we could answer using this dataset, including: What types of cars are most likely to be pulled over for speeding?

    What times of day are police most active? Or are tickets spread pretty evenly in terms of geography? What are the most common things people are pulled over for? Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation will not be published.

    A list of lists appears to be associated with data, and this likely contains each record in our traffic violations dataset.

    Each inner list is a record, and the first record appears in the output from the grep command. We may be able to find this information under the meta key. From the head command, we know that there are at least 3 levels of keys, with meta containing a key view, which contains the keys id, name, averageRating and others.

    In this case, the columns key looks interesting, as it potentially contains information on the columns in the list of lists in the data key. Extracting information on the columns Now that we know which key contains information on the columns, we need to read that information in. We can accomplish this using the ijson package.

    We specify the path to the list using the meta. Recall that meta is a top level key, which contains view inside, which contains columns inside it. We then specify meta. The items function will return a generator , so we use the list method to turn the generator into a Python list.

    In order to get our header, it looks like fieldName is the relevant key to extract. Now that we have our columns names, we can move to extracting the data itself. Extracting the data You may recall that the data is locked away in a list of lists inside the data key.

    Fortunately, we can use the column names we just extracted to only grab the columns that are relevant. This will save a ton of space. If the dataset was larger, you could iteratively process batches of rows. So read in the first rows, do some processing, then the next , and so on. Pandas allows you to convert a list of lists into a Dataframe and specify the column names separately. It looks like Sunday has the most stops, and Monday has the least.

    This could also be a data quality issue where invalid dates resulted in Sunday for some reason. We can also plot out the most common traffic stop times: plt. This might make sense, as people are driving home from bars and dinners late and night, and may be impaired.

    Folium allows you to easily create interactive maps in Python by leveraging leaflet. If you want to further explore this dataset, here are some interesting questions to answer: Does the type of stop vary by location? How does income correlate with number of stops? How does population density correlate with number of stops? What types of stops are most common around midnight? About the author.

    A cheat sheet for working with JSON Data in Python

    If you need help setting MicroPython on the ESP32, please check this previous post for a detailed guide. The guide also explains how to connect to the Python prompt.

    You can also check the documentation of the library at Github. Since we are going to use the command line for testing the code, we will need a tool to help us compress the JSON content in a single line, so we can easily paste it. So, we will use this website, which can receive a JSON string and compress it to a single line.

    The code After connecting to the Python prompt, we are ready to start coding. So, the first thing we need to do is importing the uJSON module. Just type the expression bellow and hit enter. We will start with a simple JSON structure, which is the one shown bellow. We will store the object in a variable called parsed. So we will print it. Additionally, we will print the type of the object with the type function. Note that the type of our object with the parsed content is a Python dictionary , making it perfect for accessing the content in a key-value style.

    In order to access the value for that key in the dictionary, send the command bellow. Note that it is like accessing an array value but instead of using an index, we use a key, in the format of a string. Figure 2 — Accessing the parsed values of the dictionary object. To finalize our example, we will now parse a more complex structure, as shown bellow. This could represent, for example, a message sent from an IoT device.

    As can be seen, all the values for each key are printed correctly. Naturally, this is much better since we can operate over those values with all the functions available for lists, making them easier to manipulate.

    ESP32 MicroPython: Parsing JSON

    In the dataset above, each row represents a country, and each column represents some fact about that country. This is called unstructured data. A good example is a list of events from visitors on a website. Each event has different fields, and some of the fields are nested within other fields.

    This type of data is very hard to store in a regular SQL database. JSON is a way to encode data structures like lists and dictionaries to strings that ensures that they are easily readable by machines. Python has great JSON support, with the json library. We can both convert lists and dictionaries to JSON, and convert strings to lists and dictionaries. JSON data looks much like a dictionary would in Python, with keys and values stored.

    You can download the data here. The data contains information about where the violation happened, the type of car, demographics on the person receiving the violation, and some other interesting information. There are quite a few questions we could answer using this dataset, including: What types of cars are most likely to be pulled over for speeding? What times of day are police most active? Or are tickets spread pretty evenly in terms of geography?

    What are the most common things people are pulled over for? Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation will not be published. A list of lists appears to be associated with data, and this likely contains each record in our traffic violations dataset.

    Each inner list is a record, and the first record appears in the output from the grep command. We may be able to find this information under the meta key. From the head command, we know that there are at least 3 levels of keys, with meta containing a key view, which contains the keys id, name, averageRating and others. In this case, the columns key looks interesting, as it potentially contains information on the columns in the list of lists in the data key. Extracting information on the columns Now that we know which key contains information on the columns, we need to read that information in.

    We can accomplish this using the ijson package.

    How can i convert json file from labelme interface to png or image format file?

    So, we will use this website, which can receive a JSON string and compress it to a single line. The code After connecting to the Python prompt, we are ready to start coding. So, the first thing we need to do is importing the uJSON module.

    Just type the expression bellow and hit enter.

    How to use Python 3 to save an image via a JSON payload to a SAP HANA OData endpoint

    We will start with a simple JSON structure, which is the one shown bellow. We will store the object in a variable called parsed. So we will print it. Additionally, we will print the type of the object with the type function. Note that the type of our object with the parsed content is a Python dictionarymaking it perfect for accessing the content in a key-value style. In order to access the value for that key in the dictionary, send the command bellow.


    Json to png python