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For Each Loop to convert into numeric values


For Each Loop to convert into numeric values

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For Each Loop to convert into numeric values
Tag : r , By : Nick
Date : November 23 2020, 04:01 AM

this one helps. If it is already a factor variable, then simply convert to integer,i.e.
employ.data$location <- as.integer(employ.data$location)
employ.data
#    employee salary  startdate location
#1   John Doe  21000 2010-11-01        2
#2 Peter Gynn  23400 2008-03-25        1
#3 Jolie Hope  26800 2007-03-14        2
employ.data$location <- as.integer(as.factor(employ.data$location))

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Have a Crystal Reports formula convert numeric strings to values, but leave non-numeric blank/null


Tag : crystal-reports , By : Genipro
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a string field that mostly contains numeric decimal values, but sometimes contains values like "<0.10" or "HEMOLYSIS". , In the past, I've created a SQL Expression that returns a NULL:
-- {@DB_NULL}
-- Oracle syntax
(
SELECT NULL FROM DUAL
)
-- {@DB_NULL}
-- MS SQL syntax
(
SELECT NULL
)
// {@FormulaField}
If IsNumberic({table.field} Then
  ToNumber({table.field})
Else
  ToNumber({@DB_NULL})

How do I test for numeric values in a dataframe of characters, and convert those to numeric?


Tag : r , By : unadopted
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have a dataframe somewhat like the following: , Looks like a job for type.convert().
theDF[] <- lapply(theDF, type.convert, as.is = TRUE)
## check the result
sapply(theDF, class)
#          ID          Ticker INDUSTRY_SECTOR             VAR            CVAR 
#   "integer"     "character"     "character"       "numeric"       "numeric" 
theDF[] <- lapply(theDF, function(x) type.convert(as.character(x), as.is = TRUE))

Check if all values are numeric over multiple columns and convert them to numeric


Tag : r , By : codelurker
Date : March 29 2020, 07:55 AM
help you fix your problem We can use parse_guess function from readr package which basically tries to guess the type of columns.
library(readr)
library(dplyr)

df1 <- df %>% mutate_all(parse_guess)


str(df1)
#'data.frame':  16 obs. of  11 variables:
# $ ID         : chr  "A" "A" "A" "A" ...
# $ ToolID     : chr  "CCP_A" "CCP_A" "CCQ_A" "CCQ_A" ...
# $ Step       : chr  "Step_A" "Step_A" "Step_B" "Step_C" ...
# $ Measurement: chr  "Length" "Breadth" "Width" "Height" ...
# $ Passfail   : chr  "Pass" "Pass" "Fail" "Fail" ...
# $ Points     : int  7 5 3 4 0 0 0 0 17 15 ...
# $ Average    : num  7.5 6.5 7.1 6.6 NA NA NA NA 17.5 16.5 ...
# $ Sigma      : num  2.5 2.5 2.1 2.6 NA NA NA NA 12.5 12.5 ...
# $ Tool       : chr  "ABC_1" "ABC_2" "ABD_1" "ABD_2" ...
# $ Dose       : num  NA NA NA NA 17.1 NA NA 17.3 NA NA ...
# $ Machine    : chr  "CO2" "CO6" "CO3" "CO6" ...

How to convert pandas data frame string values to numeric values


Tag : python , By : Steve M
Date : March 29 2020, 07:55 AM
wish of those help In this case you can use the datatype category of pandas to map strings to indices (see categorical data). So it's not necessary to use LabelEncoder or OneHotEncoder of scikit-learn.
import pandas as pd

df = pd.read_csv('54055554.csv', header=0, dtype={
    'type': 'category',  # <--
    'amount': float,
    'nameOrig': str,
    'oldbalanceOrg': float,
    'newbalanceOrig': float,
    'nameDest': str,
    'oldbalanceDest': float,
    'newbalanceDest': float,
    'isFraud': bool,
    'isFlaggedFraud': bool
})

print(dict(enumerate(df['type'].cat.categories)))
# {0: 'PAYMENT', 1: 'TRANSFER'}

print(list(df['type'].cat.codes))
# [0, 0, 1]
type, ...
PAYMENT, ...
PAYMENT, ...
TRANSFER, ...

Cannot convert alpha-numeric values in a dataset to just numeric


Tag : r , By : Amin Amini
Date : March 29 2020, 07:55 AM
To fix the issue you can do It looks like you have a named vector, where, e.g., the name "m/z = 899.91" has the value 0.6186..., the name "m/z = 900.36" has the value 1.35811..., etc. Your vector is numeric, and the values are all those less-than-one parts.
The 899.91, 900.36, etc. are part of the names. So as.numeric(gsub("m/z = ", "", names(data))) should get you the numeric part of the names.
my_data = data.frame(
  mz = as.numeric(gsub("m/z = ", "", names(data))),
  intensity = data
) 
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