falcon | Free , open-source SQL client for Windows and Mac 🦅 | Data Visualization library
kandi X-RAY | falcon Summary
kandi X-RAY | falcon Summary
Falcon is a free, open-source SQL editor with inline data visualization. It currently supports connecting to RedShift, MySQL, PostgreSQL, IBM DB2, Impala, MS SQL, Oracle, SQLite and more (for connecting to Oracle, please, see here the instructions to install the required free Oracle Instant Client). Heads up! Falcon is open source and works without an internet connection or a Plotly Chart Studio account. The software works well and we hope you find it useful. FYI, as a company we are spending most of our development effort on Dash Open Source & Dash Enterprise. Truth be told, we found that most companies we worked with preferred to own the analytical backend. With Dash, we provide the visualization and application primitives so that you can build your own tailor-made dashboards, analytical apps, or yes, even, SQL editors. If you are wondering what we're up to lately, check out our latest post on our Dash vision: Dash is bringing React to Python, R, and Julia. Visit plot.ly to learn more or visit the Plotly forum. Follow @plotlygraphs on Twitter for Falcon announcements. Download Falcon for Mac OS. Download Falcon for Windows. Download Falcon for Linux.
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Top functions reviewed by kandi - BETA
- Parse an Elasticsearch query response from an input .
- Sets up the templates for the page .
- Merge state of selected state
- Updates the grid of a given column names
- Derives the settings from the given setting .
- Set the authentication authorization page
- Connects to the CSV file
- Delete a tab from the current tab
- Parses an SQL string
- Returns a promise that resolves once the query is completed .
falcon Key Features
falcon Examples and Code Snippets
Community Discussions
Trending Discussions on falcon
QUESTION
I can't find a solution online and I know this should be easy but I can't figure out what is wrong with my regex:
here is my code:
...ANSWER
Answered 2022-Mar-17 at 17:34You can use .str.extract
, convert each row of results to a list, and then use .str.join
(and of course concatenate a +
at the beginning):
QUESTION
I am trying to get the first value of the the list in each row of df['Emails'] but in real life (this is a sample df) I don't know what the length of the list will be so I am just assuming that the longest will be length of 5 and then trying to whittle it down until I find the right length and selecting that index position but I am getting IndexError: index 5 is out of bounds for axis 0 with size 2
and I can't figure out what to do about it. Any help appreciated. Thanks.
my current code:
...ANSWER
Answered 2022-Mar-15 at 23:36Whenever you have a column containing lists, explode
will often be your friend, and this is the case here.
Use explode
, groupby(level=0)
(to group on the 0th (first) level of the index), and first
(which selects the first non-null value (including None, NaN, etc.))
QUESTION
Background
I have a complex nested JSON object, which I am trying to unpack into a pandas df
in a very specific way.
JSON Object
this is an extract, containing randomized data of the JSON object, which shows examples of the hierarchy (inc. children) for 1x family (i.e. 'Falconer Family'), however there is 100s of them in total and this extract just has 1x family, however the full JSON object has multiple -
ANSWER
Answered 2022-Feb-16 at 06:41I think this gets you pretty close; might just need to adjust the various name
columns and drop the extra data (I kept the grouping
column).
The main idea is to recursively use pd.json_normalize with pd.concat for all availalable children
levels.
EDIT: Put everything into a single function and added section to collapse the name
columns like the expected output.
QUESTION
I have a large list of models that I built using lapply with the following code (these lists are too long to show the whole data but I used the corresponding code to set the models up):
...ANSWER
Answered 2022-Feb-16 at 01:07You need an iterator to move through both the models and the new data. Instead of moving through the models, make it an iterator.
QUESTION
[
0: {_id: '61de38eb6ea1563609e1d0a7', title: 'FALCON SR SUNTOUR', price: '59', description: ' Alloy.., …}
1: {_id: '61d7a8b885c68311be8dd1b3', title: 'Lifelong LLBC2702 Falcon', price: '59', description: 'Low Maintenance: High.., …}
]
...ANSWER
Answered 2022-Jan-21 at 04:38You cannot call map
on each order item as it is an object
. To iterate over them use Object.entries
method.
Try like below
QUESTION
I have the following Pandas DataFrame and I am trying to group animals according to their class. I know I can use groupby to get a faster result. However, I was thinking if there was a way to replicate the groupby function by iterating over the rows.
...ANSWER
Answered 2021-Dec-24 at 20:33You don't really need a loop for any of this. First get a list of the unique elements:
QUESTION
Can I get the value of grouped column in apply in pandas groupby? For example,
...ANSWER
Answered 2021-Nov-25 at 14:44IIUC use x.name
:
QUESTION
I have a spreadsheet of fantasy players and their individual game stats. What I would like to add is a column that lists the Vegas Line of that individual game.
I'm merging from the below spreadsheet:
...ANSWER
Answered 2021-Nov-24 at 20:43Try changing x.lstrip('at')
to x.lstrip('at ')
QUESTION
I have downloaded a list of all the towns and cities etc in the US from the census bureau. Here is a random sample:
...ANSWER
Answered 2021-Nov-12 at 22:48I have such a solution. And I'm surprised myself that I used two loops for
!! Incredibly, I did it. First things first.
My proposal is based on a simplification. However, the mistake you will make at short distances will be relatively small. But the time gain is huge!
Well, I propose to count the distance in Cartesian coordinates, not spherical.
So we're going to need a simple function that computes the Cartesian coordinates based on the two arguments latitude
and longitude
.
Here is our LatLong2Cart
feature.
QUESTION
I have the following working code. I need to add a percentage column to monitor changes. I dont know much on how to do it in pandas. I need ideas on what part needs to be modified.
...ANSWER
Answered 2021-Nov-09 at 18:18Setup:
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