A Data frame is a two-dimensional data structure, i. For the row labels, the Index to be used for the resulting frame is Optional Default np.
For column labels, the optional default syntax is - np. This is only true if no index is passed. In the subsequent sections of this chapter, we will see how to create a DataFrame using these inputs. All the ndarrays must be of same length. If index is passed, then the length of the index should equal to the length of the arrays.
If no index is passed, then by default, index will be range nwhere n is the array length. They are the default index assigned to each using the function range n.
List of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names. The following example shows how to create a DataFrame by passing a list of dictionaries and the row indices. The following example shows how to create a DataFrame with a list of dictionaries, row indices, and column indices.
Dictionary of Series can be passed to form a DataFrame. The resultant index is the union of all the series indexes passed. We will now understand row selection, addition and deletion through examples.
Let us begin with the concept of selection. The result is a series with labels as column names of the DataFrame. And, the Name of the series is the label with which it is retrieved.
Add new rows to a DataFrame using the append function. This function will append the rows at the end. Use index label to delete or drop rows from a DataFrame.
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If label is duplicated, then multiple rows will be dropped. If you observe, in the above example, the labels are duplicate. Let us drop a label and will see how many rows will get dropped. Python Pandas - DataFrame Advertisements. Previous Page. Next Page. Live Demo.
The Pandas DataFrame – loading, editing, and viewing data in Python
Previous Page Print Page.This document is written as a Jupyter Notebook, and can be viewed or downloaded here. You can apply conditional formattingthe visual styling of a DataFrame depending on the data within, by using the DataFrame. This is a property that returns a Styler object, which has useful methods for formatting and displaying DataFrames.
The styling is accomplished using CSS. These functions can be incrementally passed to the Styler which collects the styles before rendering. Both of those methods take a function and some other keyword arguments and applies your function to the DataFrame in a certain way. For Styler. Note : The DataFrame. If you want the actual HTML back for further processing or for writing to file call the. We can view these by calling the. Pandas matches those up with the CSS classes that identify each cell.
That means we should use the Styler. Notice the similarity with the standard df. We want you to be able to reuse your existing knowledge of how to interact with DataFrames. This will be a common theme. Finally, the input shapes matched. Now suppose you wanted to highlight the maximum value in each column. In this case the input is a Seriesone column at a time. We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain.
Above we used Styler. Internally, Styler. What if you wanted to highlight just the maximum value in the entire table? When using Styler.Ping us in the community forum.
There are a few ways to display data tables, arrays, data frames in Streamlit apps. In getting startedyou were introduced to magic and st. This example uses Numpy to generate a random sample, but you can use Pandas DataFrames, Numpy arrays, or plain Python arrays. To install Jinja2, run: pip install jinja2.
Streamlit also has a method for static table generation: st. You can use the st. In Streamlit, you can not only replace entire elements in your app, but also modify the data behind those elements.
Here is how:. Coming soon! Streamlit 0. Sometimes you want to draw it another way. For example, instead of drawing a dataframe as an interactive table, you may want to draw it as a static table by using st.
The second reason is that other methods return an object that can be used and modified, either by adding data to it or replacing it. Finally, if you use a more specific Streamlit method you can pass additional arguments to customize its behavior. DataFrame np.
We'll use this later. Here is how: import numpy as np import time Get some data.Analyzing datasets with dates and times is often very cumbersome.
Months of different lengths, different distributions of weekdays and weekends, leap years, and the dreaded timezones are just a few things you may have to consider depending on your context. For this reason, Python has a data type specifically designed for dates and times called datetime.
However, in many datasets, you'll find that dates are represented as strings. So, in this tutorial, you'll learn how to convert date strings to the datetime format and see how you can use its powerful set of tools to work effectively with complicated time series data. The main challenge is often specifying how date strings are expressed. These all inform you of the same date, but you can probably imagine that the code to convert each of these is slightly different.
Take a moment to examine the function calls below:. First, the datetime type is imported from the datetime module. Then, the date string is passed to the. You can combine directives, special characters e. As you can see, the resulting datetime objects are identical because all three date strings represent the same date. You can find the full list of directives in the Python Documentationbut below is a table most relevant to what you saw above:.
Now that you're familiar with Python's strptime directives, let's learn how to convert a whole column of date strings in a dataset to the datetime format. From now on, you'll be working with a DataFrame called eth that contains some historical data on ether, a cryptocurrency whose blockchain is generated by the Ethereum platform.
Your dataset has the following columns:. Here are the first few rows of your dataset. Note how the dates are represented so you can use the right directives later:. The date column is indeed a string, which—remember—is denoted as an object type in Python. You can convert it to the datetime type with the. The console below contains the call to convert the column. Can you complete it by specifying the directives according to how dates are expressed in your dataset?
Now that you have datetime objects as your date column, you can extract specific components of the date such as the month, day, or year, all of which are available as the object's attributes:. Date attributes are frequently used to group data by a particular time frame. For example, you can see how many ethers were generated on a yearly basis:.
Another common case when working with dates is to get a date 30, 60, or 90 days in the past from some date.Working with Jinja templates - Python on the web - Learning Flask Series Pt. 6
In Python, the timedelta object from the datetime module is used to represent differences in datetime objects. You can create a timedelta by passing any number of keyword arguments such as dayssecondsmicrosecondsmillisecondsminuteshoursand weeks to it. Once you have a timedelta object, you can add or subtract it from a datetime object to get another datetime object. Try it in the console below:.Pandas is excellent at manipulating large amounts of data and summarizing it in multiple text and visual representations.
Where things get more difficult is if you want to combine multiple pieces of data into one document. For example, if you want to put two DataFrames on one Excel sheet, you need to use the Excel libraries to manually construct your output.
It is certainly possible but not simple. This article will describe one method to combine multiple pieces of information into an HTML template and then converting it to a standalone PDF document using Jinja templates and WeasyPrint. Before going too far through this article, I would recommend that you review the previous articles on Pandas Pivot Tables and the follow-on article on generating Excel reports from these tables. As shown in the reporting articleit is very convenient to use Pandas to output data into multiple sheets in an Excel file or create multiple Excel files from pandas DataFrames.
However, if you would like to combine multiple pieces of information into a single file, there are not many simple ways to do it straight from Pandas. The nice thing about this approach is that you can substitute your own tools into this workflow. Plug in mako or your templating tool of choice. First, I decided to use HTML as the templating language because it is probably the simplest way to generate structured data and allow for relatively rich formatting. I also think everyone knows or can figure out enough HTML to generate a simple report.
There are certainly other options out there so feel free to experiment with your options. As an alternative, I have used xhtml2pdf in the past and it works well too. Generate some overall descriptive statistics about the entire data set. I have one quick aside before we talk templates.
For some quick and dirty needs, sometimes all you need to do is copy and paste the data. Jinja templating is very powerful and supports a lot of advanced features such as sandboxed execution and auto-escaping that are not necessary for this application.
The other key component is the creation of env. This variable is how we pass content to our template. The final step is to render the HTML with the variables included in the output. The PDF creation portion is relatively simple as well. The mechanism we have to use to style is CSS. Every time I start playing with it I feel like I spend more time monkeying with the presentation than I did getting the data summarized. There is still a lot more you can do with it but this shows how to make it at least serviceable for a start.
The include allows us to bring in a snippet of HTML and use it repeteadly in different portions of the code. You may also notice that we use a pipe to round each value to 1 decimal place. There is also a for loop that allows us to display the details for each manager in our report.The attribute used for each column in the declaration of the column is used as the default thing to lookup in each item.
There are also LinkCol and ButtonCol that allow links and buttons, which is where the Flask-specific-ness comes in. Note that a and b define an attribute on the table class, but c defines an attribute on the instance, so anything set like in c will override anything set in a or b.
OptCol - converts values according to a dictionary of choices. Eg for turning stored codes into human readable text. ButtonCol subclass of LinkCol creates a button that posts the the given address. NestedTableCol - allows nesting of tables inside columns.
When creating the column, you pass some choices. This should be a dict with the keys being the values that will be found on the item's attribute, and the values will be the text to be displayed. The default value will be used if the value found from the item isn't in the choices dict. The default key works in much the same way, but means that if your default is already in your choices, you can just point to it rather than repeat it.
So the value from the item is coerced to a bool and then looked up in the choices to get the text to display. Formats a date from the item.
Formats a datetime from the item. Gives a way of putting a link into a td. You must specify an endpoint for the url. These keys obey the same rules as elsewhere, so can be things like 'category. This can be useful for adding constant GET params to a url.
The text for the link is acquired in almost the same way as with other columns. This make more sense for things like an "Edit" link. Has all the same options as LinkCol but instead adds a form and a button that gets posted to the url.Previously I was using powerlevel9k as theme for my iTerm2 Zsh configuration.
Recently I had to install a new MacBook and found an easier way to make the terminal look fancier. For Mac it is as simple as the following few lines, assuming you have brew installed.
In the following article I show a quick example how I connect to Redshift and use the S3 setup to write the table to file. Last week I was trying to connect to S3 again using Spark on my local machine, but I wasn't able to read data from our datalake.
Our datalake is hosted in the eu-west-2 region which apparently requires you to specify the version of authentication. Instead of setting up the right environment on my machine and reconfigure everything, I chose to update the Docker image from my notebook repo so I could test on my Mac before pushing it to my server. Instead of configuring both my local and remote environment I can simply spin up the Docker container and have two identical environments. In this project I want to verify the availability of the APIs that we use to ingest data into our data platform.
First I will create a test suite to verify the availability and once this works move it to a Lambda function that could be scheduled with CloudWatch on a fixed schedule. Maintaining a Latex document is cumbersome and it is difficult to divide the data from the style.
By using Jinja it is straightforward to separate the resume data from the actual layout. And the most important part, I can stick to Python! A simple introduction to create fake data using the Faker tool in Python. Very convenient if you need to generate dummy data for an experiment. In my two previous articles Unittesting in a Jupyter notebook and Mocking in unittests in Python I have discussed the use of unittest and mock to run tests for a simple Castle and Character class.
For the code behind this article please check Github. Reading big files was failing for me when I was using plain Python with pysftp. In this article I will create an abstract class and different concrete classes to be used within AWS Lambda deployed with Terraform. Toggle navigation JJ's World. Python PySpark Redshift dataframe Spark. Python PySpark trick dataframe. Python testing unittest pytest mock.