pandapy | Pandas 10x to 50x faster
kandi X-RAY | pandapy Summary
kandi X-RAY | pandapy Summary
A Structured NumPy Array is an array of structures. NumPy arrays can only contain one data type, but structured arrays in a sense create an array of homogeneous structures. This is done without moving out of NumPy such as is required with Xarray. For structured arrays the data type only has to be the same per column like an SQL data base. Each column can be another multidimensional object and does not have to conform to the basic NumPy datatypes. PandaPy comes with similar functionality like Pandas, such as groupby, pivot, and others. The biggest benefit of this approach is that NumPy dtype(data type) directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program. If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful.
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Top functions reviewed by kandi - BETA
- Fill missing values with NaN
- Concatenate two arrays
- Returns the column names of the numpy array
- Fill missing values
- Merge two arrays
- View the specified fields
- Drop fields from an array
- Describe an array
- Format a numpy array
- Create a pivot table
- Group arrays by groupby functions
- Read data from file
- Add values to an ndarray
- Find dates in a numpy array
- Return True if the string is a date
- Add values to a column
- Automatically Regression
- Calculate the moving average of an array
- Returns the return value of a column
- Shift the array by steps
- Calculate the correlation ratio
- Shift lag of a feature
pandapy Key Features
pandapy Examples and Code Snippets
Community Discussions
Trending Discussions on Big Data
QUESTION
I have a hive table that records user behavior
like this
userid behavior timestamp url 1 view 1650022601 url1 1 click 1650022602 url2 1 click 1650022614 url3 1 view 1650022617 url4 1 click 1650022622 url5 1 view 1650022626 url7 2 view 1650022628 url8 2 view 1650022631 url9About 400GB is added to the table every day.
I want to order by timestamp asc, then one 'view' is in a group between another 'view' like this table, the first 3 lines belong to a same group , then subtract the timestamps, like 1650022614 - 1650022601 as the view time.
How to do this?
i try lag and lead function, or scala like this
...ANSWER
Answered 2022-Apr-15 at 12:43If you use dataframe, you can build partition by using window that sum a column whose value is 1 when you change partition and 0 if you don't change partition.
You can transform a RDD to a dataframe with sparkSession.createDataframe() method as explained in this answer
Back to your problem. In you case, you change partition every time column behavior is equal to "view". So we can start with this condition:
QUESTION
Here is the scenario. Assuming I have the following table:
identifier line 51169081604 2 00034886044 22 51168939455 52The challenge is to, for every single column line, select the next biggest column line, which I have accomplished by the following SQL:
...ANSWER
Answered 2022-Apr-10 at 20:21Using your "next" approach AND assuming the data is generated in ascending line order, the following does work in parallel, but if actually faster you can tell me; I do not know your volume of data. In any event you cannot solve just with SQL (%sql).
Here goes:
QUESTION
I want to store +3 millions records in my Firestore database and I would like to know what is the best way, practice, to do that.
In fact, I want to store every prices of 30 cryptos every 15 minutes since 01/01/2020.
For example:
- ETH price at 01/01/2020 at 00h00 = xxx
- ETH price at 01/01/2020 at 00h15 = xxx
- ETH price at 01/01/2020 at 00h30 = xxx
- ...
- ETH price at 09/04/2022 at 14h15 = xxx
and this, for 30 cryptos (or more).
So, 120 prices per day multiplied by 829 days multiplied by 30 cryptos ~= 3M records
I thought of saving this like this:
[Collection of Crypto] [Document of crypto] [Collection of dates] [Document of hour] [Price]
I don't know if this is the right way, that's why I come here :)
Of course, the goal of this database will be to retrieve ALL the historical prices of a currency that I would have selected. This will allow me to make statistics etc later.
Thanks for your help
...ANSWER
Answered 2022-Apr-09 at 13:18For the current structure, instead of creating a document every 15 minutes you can just create a "prices" document and store an array of format { time: "00:00", price: 100 } which will cost only 1 read to fetch prices of a given currency on a day instead of 96.
Alternatively, you can create a single collection "prices" and create a document everyday for each currency. A document in this collection can look like this:
QUESTION
When I execute run-example SparkPi, for example, it works perfectly, but
when I run spark-shell, it throws these exceptions:
ANSWER
Answered 2022-Jan-07 at 15:11i face the same problem, i think Spark 3.2 is the problem itself
switched to Spark 3.1.2, it works fine
QUESTION
New to R, my apologies if there is an easy answer that I don't know of.
I have a dataframe with 127.124 observations and 5 variables
Head(SortedDF)
...ANSWER
Answered 2022-Mar-30 at 08:31library(tidyverse)
data <- tibble(x = c(1, 1, 2), y = "a")
data
#> # A tibble: 3 × 2
#> x y
#>
#> 1 1 a
#> 2 1 a
#> 3 2 a
same_rows <-
data %>%
# consider all columns
unite(col = "all") %>%
transmute(same_as_next_row = all == lead(all))
data %>%
bind_cols(same_rows)
#> # A tibble: 3 × 3
#> x y same_as_next_row
#>
#> 1 1 a TRUE
#> 2 1 a FALSE
#> 3 2 a NA
QUESTION
I have a dataset that includes video frames partially 1000 real videos and 1000 deep fake videos. each video after preprocessing phase converted to the 300 frames in other worlds I have a dataset with 300000 images with Real(0) label and 300000 images with Fake(1) label. I want to train MesoNet with this data. I used costum DataGenerator class to handle train, validation, test data with 0.8,0.1,0.1 ratios but when I run the project show this message:
...ANSWER
Answered 2021-Nov-10 at 14:23Note that this is not an error, but a log message: https://github.com/tensorflow/tensorflow/blob/42b5da6659a75bfac77fa81e7242ddb5be1a576a/tensorflow/core/kernels/data/shuffle_dataset_op.cc#L138
It seems you may be choosing too large a dataset if it's taking too long: https://github.com/tensorflow/tensorflow/issues/30646
You can address this by lowering your buffer size: https://support.huawei.com/enterprise/en/doc/EDOC1100164821/2610406b/what-do-i-do-if-training-times-out-due-to-too-many-dataset-shuffle-operations
QUESTION
I'm reading an article about how to design a Twitter Search. The basic idea is to map tweets based on their ids to servers where each server has the mapping
English word -> A set of tweetIds having this word
Now if we want to find all the tweets that have some word all we need is to query all servers and aggregate the results. The article casually suggests that we can also sort the results by some parameter like "popularity" but isn't that a heavy task, especially if the word is an hot word?
What is done in practice in such search systems?
Maybe some tradeoff are being used?
Thanks!
...ANSWER
Answered 2022-Mar-24 at 17:25First of all, there are two types of indexes: local and global.
A local index is stored on the same computer as tweet data. For example, you may have 10 shards and each of these shards will have its own index; like word "car" -> sorted list of tweet ids.
When search is run we will have to send the query to every server. As we don't know where the most popular tweets are. That query will ask every server to return their top results. All of these results will be collected on the same box - the one executing the user request - and that process will pick top 10 of of entire population.
Since all results are already sorted in the index itself, it is a O(1) operation to pick top 10 results from all lists - as we will be doing simple heap/watermarking on set number of tweets.
Second nice property, we can do pagination - the next query will be also sent to every box with additional data - give me top 10, with popularity below X, where X is the popularity of last tweet returned to customer.
Global index is a different beast - it does not live on the same boxes as data (it could, but does not have to). In that case, when we search for a keyword, we know exactly where to look for. And the index itself is also sorted, hence it is fast to get top 10 most popular results (or get pagination).
Since the global index returns only tweet Ids and not tweet itself, we will have to lookup tweets for every id - this is called N+1 problem - 1 query to get a list of ids and then one query for every id. There are several ways to solve this - caching and data duplication are by far most common approaches.
QUESTION
I'm trying to optimise my query for when an internal customer only want to return one result *(and it's associated nested dataset). My aim is to reduce the query process size.
However, it appears to be the exact same value regardless of whether I'm querying for 1 record (with unnested 48,000 length array) or the whole dataset (10,000 records with unnest total 514,048,748 in total length of arrays)!
So my table results for one record query:
...ANSWER
Answered 2022-Mar-24 at 11:45This is happening because there is still need for a full table scan to find all the test IDs that are equal to the specified one.
It is not clear from your example which columns are part of the timeseries record. In case test_id is not one of them, I would suggest to cluster the table on the test_id column. By clustering, the data will be automatically organized according to the contents of the test_id column.
So, when you query with a filter on that column a full scan won't be needed to find all values.
Read more about clustered tables here.
QUESTION
I am new to data handling . I need to create python program to search a record from a samplefile1 in samplefile2. i am able to achieve it but for each record out of 200 rows in samplefile1 is looped over 200 rows in samplefile2 , it took 180 seconds complete execution time.
I am looking for something to be more time efficient so that i can do this task in minimum time .
My actual Dataset size is : 9million -> samplefile1 and 9million --> samplefile2.
Here is my code using Pandas.
sample1file1 rows:
...ANSWER
Answered 2022-Mar-19 at 11:27I don't think using Pandas is helping here as you are just comparing whole lines. An alternative approach would be to load the first file as a set of lines. Then enumerate over the lines in the second file testing if it is in the set. This will be much faster:
QUESTION
I have a dataframe like this:
...ANSWER
Answered 2022-Mar-12 at 22:29It is just that there are many unused levels as the column 'seqnames' is a factor. With split, there is an option to drop (drop = TRUE - by default it is FALSE) to remove those list elements. Otherwise, they will return as data.frame with 0 rows. If we want those elements to be replaced by NULL, then find those elements where the number of rows (nrow) are 0 and assign it to NULL
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Install pandapy
You can use pandapy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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