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how to create new dataframe out of columns after resampling?


how to create new dataframe out of columns after resampling?

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how to create new dataframe out of columns after resampling?
Tag : python , By : Ravenal
Date : November 28 2020, 04:01 AM

Any of those help I have read a daily stock data csv file. and re-sample each column to weekly data. now im trying to create a new DataFrame to contain those new resampled columns.my code , As I understand you have five dataframes after re-sampling :
Open,High,Low,Close,Volume
df = pd.Dataframe(columns)
df = pd.concat([Open,High,Low,Close,Volume],axis = 1)

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Create Spark Dataframe from existing Dataframe such that new Dataframe's columns based on existing Dataframe rows


Tag : scala , By : lwl_seu
Date : March 29 2020, 07:55 AM
may help you . You can simply use groupBy() together with piviot(). Using your example dataframe:
val spark = SparkSession.builder.getOrCreate()
import spark.implicits._

val df = ...

df.show()
+------------------+----+-----+
|         Timestamp|  ID|Value|
+------------------+----+-----+
|29/08/2017 4:51:23|ID-1|  1.1|
|29/08/2017 4:52:14|ID-2|  2.1|
|29/08/2017 4:52:14|ID-3|  3.1|
|29/08/2017 4:55:23|ID-1|  1.2|
|29/08/2017 4:55:23|ID-3|  3.2|
|29/08/2017 4:57:42|ID-2|  2.2|
+------------------+----+-----+

val newDF = df.groupBy("Timestamp")
  .pivot("ID")
  .agg(sum($"Value"))

newDF.show()
+------------------+----+----+----+
|         Timestamp|ID-1|ID-2|ID-3|
+------------------+----+----+----+
|29/08/2017 4:57:42|null| 2.2|null|
|29/08/2017 4:55:23| 1.2|null| 3.2|
|29/08/2017 4:51:23| 1.1|null|null|
|29/08/2017 4:52:14|null| 2.1| 3.1|
+------------------+----+----+----+

R text mining: Create document term matrix from dataframe, convert to dataframe, retain columns from original dataframe


Tag : r , By : user176691
Date : March 29 2020, 07:55 AM
it fixes the issue The quanteda package is faster and more straightforward than tm, and works nicely with tidytext as well. Here's how to do it:
These operations create a corpus from your object, create a document-feature matrix, and then return a data.frame that combines the variables with the feature counts. (Additional options are available when creating the dfm, see ?dfm).
library("quanteda")
samplecorp <- corpus(sampletxt, text_field = "TVAR")
sampledfm <- dfm(samplecorp)
result <- cbind(docvars(sampledfm), as.data.frame(sampledfm))
dplyr::group_by(result[, 1:6], PTNO, DATE, TYPE)
# # A tibble: 5 x 6
# # Groups:   PTNO, DATE, TYPE [5]
# PTNO       DATE          TYPE  this sentence contains
# * <dbl>     <date>         <chr> <dbl>    <dbl>    <dbl>
#     1     1 2016-01-01 Progress note     1        1        1
#     2     2 2015-01-01 Progress note     5        6        6
#     3     2 2015-01-01      CAT scan     2        2        2
#     4     3 2016-02-01 Progress note     2        2        2
#     5     3 2016-02-14 Progress note     2        2        2

packageVersion("quanteda")
# [1] ‘0.99.6’

Resampling pandas dataframe and putting the results in columns, with the day as the index


Tag : python , By : user107021
Date : March 29 2020, 07:55 AM
it fixes the issue I have a dataset with power every 30 minutes. , Example data:
import pandas as pd

times = ["2016-06-01 00:00:00", "2016-06-01 00:30:00", "2016-06-01 01:00:00"]
vals = [5, 9, 12]
df = pd.DataFrame(dict(time = times, value = vals))
df["time"] = pd.to_datetime(df.time)
df["date"] = df.time.dt.date
df["time"] = df.time.dt.time

       time  value        date
0  00:00:00      5  2016-06-01
1  00:30:00      9  2016-06-01
2  01:00:00     12  2016-06-01
df.pivot(index="date", columns="time", values="value")

time        00:00:00  00:30:00  01:00:00
date                                
2016-06-01         5         9        12

Spark filter out columns and create dataFrame with remaining columns and create dataFrame with filtered columns


Tag : scala , By : Nick Pegg
Date : March 29 2020, 07:55 AM
hope this fix your issue I am new to Spark. , using select, you can select what columns you want.
val df2 = OriginalDF.select($"col1",$"col2",$"col3")
val df3 = OriginalDF.where($"col1" < 10)
val df3 = OriginalDF.filter($"col1" < 10)

How to drop the unwanted columns after resampling a dataframe


Tag : pandas , By : user107021
Date : March 29 2020, 07:55 AM
hope this fix your issue I have created a dataframe with the following code: , You could do:
close_columns = [column for column in resamp.columns if column[1] == 'close']

result = resamp[close_columns]
print(result)
                    a-LTP b-Lowest_Sell c-Highest_Buy
                    close         close         close
Timestamp                                            
2019-01-21 00:00:00   105         123.5         133.5
2019-01-21 00:01:00   111         126.5         136.5
2019-01-21 00:02:00   117         129.5         139.5
2019-01-21 00:03:00   123         132.5         142.5
2019-01-21 00:04:00   129         135.5         145.5
...                   ...           ...           ...
2019-01-21 06:52:00  2577        1359.5        1369.5
2019-01-21 06:53:00  2583        1362.5        1372.5
2019-01-21 06:54:00  2589        1365.5        1375.5
2019-01-21 06:55:00  2595        1368.5        1378.5
2019-01-21 06:56:00  2599        1370.5        1380.5

[417 rows x 3 columns]
lookup = {'a-LTP': 'resampled_LTP', 'b-Lowest_Sell': 'resampled_lowest_sell', 'c-Highest_Buy': 'resampled_highest_buy'}
result.columns = [lookup.get(column[0]) for column in result.columns]
print(result.columns)
Index(['resampled_LTP', 'resampled_lowest_sell', 'resampled_highest_buy'], dtype='object')
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