I want to train a simple neural network with PyTorch on a pandas dataframe df.
One of the columns is named "Target", and it is the target variable of the network. How can I use... moreI want to train a simple neural network with PyTorch on a pandas dataframe df.
One of the columns is named "Target", and it is the target variable of the network. How can I use this dataframe as input to the PyTorch network?
I tried this, but it doesn't work:
import pandas as pd
import torch.utils.data as data_utils
Assume df1 and df2 are two DataFrames in Apache Spark, computed using two different mechanisms, e.g., Spark SQL vs. the Scala/Java/Python API.Is there an idiomatic way to... moreAssume df1 and df2 are two DataFrames in Apache Spark, computed using two different mechanisms, e.g., Spark SQL vs. the Scala/Java/Python API.Is there an idiomatic way to determine whether the two data frames are equivalent (equal, isomorphic), where equivalence is determined by the data (column names and column values for each row) being identical save for the ordering of rows & columns?The motivation for the question is that there are often many ways to compute some big data result, each with its own trade-offs. As one explores these trade-offs, it is important to maintain correctness and hence the need to check for the equivalence/equality on a meaningful test data set. less
I am using spark-csv to load data into a DataFrame. I want to do a simple query and display the content:
val df =... moreI am using spark-csv to load data into a DataFrame. I want to do a simple query and display the content:
val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load("my.csv")
df.registerTempTable("tasks")
results = sqlContext.sql("select col from tasks");
results.show()
I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number... moreI am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:first_job <- function(x) tail(x, 1)first_job <- apply(data, 1, first_job)
structure(list(Index = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59),
FromJob = c("Senior Machine Learning Engineer", "Senior Machine Learning Engineer",
"Senior Machine Learning Engineer", "Senior Machine Learning Engineer",
"Senior Machine Learning Engineer", "Python Data Engineer (m/w/d)",
"Python Data Engineer (m/w/d)", "Python Data Engineer (m/w/d)",
"Lead Backend Developer (f/m/d)", "Lead Backend Developer (f/m/d)",
"Lead... less
I have a number of columns that I would like to remove from a data frame. I know that we can delete them individually using something like:
df$x <- NULL
But I was hoping to do... moreI have a number of columns that I would like to remove from a data frame. I know that we can delete them individually using something like:
df$x <- NULL
But I was hoping to do this with fewer commands.
Also, I know that I could drop columns using integer indexing like this:
df <- df
But I am concerned that the relative position of my variables may change.
Given how powerful R is, I figured there might be a better way than dropping each column one by one.
I'm just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for Dataset) in Apache Spark?Can you convert one to the other?
I'd like to take data of the form
before = data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))
attr type
1 1 ... moreI'd like to take data of the form
before = data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))
attr type
1 1 foo_and_bar
2 30 foo_and_bar_2
3 4 foo_and_bar
4 6 foo_and_bar_2
and use split() on the column "type" from above to get something like this:
attr type_1 type_2
1 1 foo bar
2 30 foo bar_2
3 4 foo bar
4 6 foo bar_2
I came up with something unbelievably complex involving some form of apply that worked, but I've since misplaced that. It seemed far too complicated to be the best way. I can use strsplit as below, but then unclear how to get that back into 2 columns in the data frame.
> strsplit(as.character(before$type),'_and_')
"foo" "bar"
"foo" "bar_2"
"foo" "bar"
"foo" "bar_2"
Thanks for any pointers. I've not quite groked R lists just yet. less
I want to get a list of the column headers from a pandas DataFrame. The DataFrame will come from user input so I won't know how many columns there will be or what they will be... moreI want to get a list of the column headers from a pandas DataFrame. The DataFrame will come from user input so I won't know how many columns there will be or what they will be called.
For example, if I'm given a DataFrame like this:
>>> my_dataframe
y gdp cap
0 1 2 5
1 2 3 9
2 8 7 2
3 3 4 7
4 6 7 7
5 4 8 3
6 8 2 8
7 9 9 10
8 6 6 4
9 10 10 7
I would get a list like this:
>>> header_list