I have pandas dataframe on which I need to some data manipulation, the following code provide me the average of column "Variable" group by "Key":
df.groupby('key').Variable.transform("mean")
The advantage of using "transform" is that it return back the result with the same index which is pretty useful.
Now, I want to have my customize function and use it within "transform" instead of "mean" more over my function need two or more column something like:
lambda (Variable, Variable1, Variable2): (Variable + Variable1)/Variable2
(actual function of mine is more complicated than this example) and each row of my dataframe has Variable,Variable1 and Variable2.
I am wondering if I can define and use such a customized function within "transform" to be able to rerun the result back with same index?
Thanks,
Amir
Don't call transform against Variable, call it on the grouper and then call your variables against the dataframe the function receives as argument:
df.groupby('key').transform(lambda x: (x.Variable + x.Variable1)/x.Variable2)
Why didn't you use simple
df.Variable + df.Variable1 / df.Variable2
There is no need to groupby. In case for example you want to divide by df.groupby('key').Variable2.transform("mean") you can still do it with transform as following:
df.Variable + df.Variable1 / df.groupby('key').Variable2.transform("mean")
Related
I'm trying to run a function (row_extract) over a column in my dataframe, that returns three values that I then want to add to three new columns.
I've tried running it like this
all_data["substance", "extracted name", "name confidence"] = all_data["name"].apply(row_extract)
but I get one column with all three values. I'm going to iterate over the rows, but that doesn't seem like a very efficient system - any thoughts?
This is my current solution, but it takes an age.
for index, row in all_data.iterrows():
all_data.at[index, "substance"], all_data.at[index, "extracted name"], all_data.at[index, "name confidence"] = row_extract(row["name"])
Check what the type of your function output is or what the datatypes are. It seems like that's a string.
You can use the "split" method on a string to separate them.
https://docs.python.org/2/library/string.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.split.html
Alternatively, adjust your function to return more than one value.
E.g.
def myfunc():
...
...
return x, y, z
Aim: To speed up applying a function row wise across a large data frame (1.9 million ~ rows)
Attempt: Using dask map_partitions where partitions == number of cores. I've written a function which is applied to each row, creates a dict containing a variable number of new values (between 1 and 55). This function works fine standalone.
Problem: I need a way to combine the output of each function into a final dataframe. I tried using df.append, where I'd append each dict to a new dataframe and return this dataframe. If I understand the Dask Docs, Dask should then combine them to one big DF. Unfortunately this line is tripping an error (ValueError: could not broadcast input array from shape (56) into shape (1)). Which leads me to believe it's something to do with the combine feature in Dask?
#Function to applied row wise down the dataframe. Takes a column (post) and new empty df.
def func(post,New_DF):
post = str(post)
scores = OtherFUNC.countWords(post)
scores['post'] = post
New_DF = New_DF.append(scores, ignore_index=True)
return(New_DF)
#Dask
dd.from_pandas(dataset,npartitions=nCores).\
map_partitions(
lambda df : df.apply(
lambda x : func(x.post,New_DF),axis=1)).\
compute(get=get)
I am not quite sure I completely understand your code in lieu of an MCVE but I think there is a bit of a misunderstanding here.
In this piece of code you take a row and a DataFrame and append one row to that DataFrame.
#Function to applied row wise down the dataframe. Takes a column (post) and new empty df.
def func(post,New_DF):
post = str(post)
scores = OtherFUNC.countWords(post)
scores['post'] = post
New_DF = New_DF.append(scores, ignore_index=True)
return(New_DF)
Instead of appending to New_DF, I would recommend just returning a pd.Series which df.apply concatenates into a DataFrame. That is because if you are appending to the same New_DF object in all nCores partitions, you are bound to run into trouble.
#Function to applied row wise down the dataframe. Takes a row and returns a row.
def tobsecret_func(row):
post = str(row.post)
scores = OtherFUNC.countWords(post)
scores['post'] = post
length_adjusted_series = pd.Series(scores).reindex(range(55))
return(length_adjusted_series)
Your error also suggests that as you wrote in your question, your function creates a variable number of values. If the pd.Series you return doesn't have the same shape and column names, then df.apply will fail to concatenate them into a pd.DataFrame. Therefore make sure you return a pd.Series of equal shape each time. This question shows you how to create pd.Series of equal length and index: Pandas: pad series on top or bottom
I don't know what kind of dict your OtherFUNC.countWords returns exactly, so you may want to adjust the line:
length_adjusted_series = pd.Series(scores).reindex(range(55))
As is, the line would return a Series with an index 0, 1, 2, ..., 54 and up to 55 values (if the dict originally had less than 55 keys, the remaining cells will contain NaN values).
This means after applied to a DataFrame, the columns of that DataFrame would be named 0, 1, 2, ..., 54.
Now you take your dataset and map your function to each partition and in each partition you apply it to the DataFrame using apply.
#Dask
dd.from_pandas(dataset,npartitions=nCores).\
map_partitions(
lambda df : df.apply(
lambda x : func(x.post,New_DF),axis=1)).\
compute(get=get)
map_partitions expects a function which takes as input a DataFrame and outputs a DataFrame. Your function is doing this by using a lambda function that basically calls your other function and applies it to a DataFrame, which in turn returns a DataFrame. This works but I highly recommend writing a named function which takes as input a DataFrame and outputs a DataFrame, it makes it easier for you to debug your code.
For example with a simple wrapper function like this:
df_wise(df):
return df.apply(tobsecret_func)
Especially as your code gets more complex, abstaining from using lambda functions that call non-trivial code like your custom func and instead making a simple named function can help you debug because the traceback will not just lead you to a line with a bunch of lambda functions like in your code but will also directly point to the named function df_wise, so you will see exactly where the error is coming from.
#Dask
dd.from_pandas(dataset,npartitions=nCores).\
map_partitions(df_wise,
meta=df_wise(dd.head())
).\
compute(get=get)
Notice that we just fed dd.head() to df_wise to create our meta-keyword which is similar to what Dask would do under the hood.
You are using dask.get, the synchronous scheduler which is why the whole New_DF.append(...) code could work, since you append to the DataFrame for each consecutive partition.
This does not give you any parallelism and thus will not work if you use one of the other schedulers, all of which parallelise your code.
The documentation also mentions the meta keyword argument, which you should supply to your map_partitions call, so dask knows what columns your DataFrame will have. If you don't do this, dask will first have to do a trial run of your function on one of the partitions and check what the shape of the output is before it can go ahead and do the other partitions. This can slow down your code by a ton if your partitions are large; giving the meta keyword bypasses this unnecessary computation for dask.
First of all, I have created a function with input of lat Lon in order to filter ships not entering a particular zone.
check_devaiation_notInZone(LAT, LON)
It takes two inputs and return True if ships not enter a particular zone.
Secondly, I got data on many ships with Lat in one header and Lon in another header in CSV format. So, I need to take data from two column into the function and create another column to store the output of the function.
After I looked at Pandas: How to use apply function to multiple columns. I found the solution df1['deviation'] = df1.apply(lambda row: check_devaiation_notInZone(row['Latitude'], row['Longitude']), axis = 1)
But I have no idea why it works. Can anyone explain the things inside the apply()?
A lambda function is just like a normal function but it has no name and can be used only in the place where it is defined.
lambda row: check_devaiation_notInZone(row['Latitude'], row['Longitude'])
is the same as:
def anyname(row):
return check_devaiation_notInZone(row['Latitude'], row['Longitude'])
So in the apply you just call another function check_devaiation_notInZone with the parameters row['Latitude'], row['Longitude'].
I'm having trouble in correctly executing a for loop through my dataframe in python.
Basically, for every row in the dataframe (df_weather), the code should select one value each from the column no. 13 and 14 and execute a function which is defined earlier in the code. Eventually, I require the calculated value in each row to be summed to give me one final answer.
The error being returned is as follows: "string indices must be integers"
Request anyone to help me through this step. The code for the same is provided below.
Thanks!
stress_rate = 0
for i in df_weather:
b = GetStressDampHeatParameterized(i[:,13], i[:,14])
stress_rate = b + stress_rate
print(stress_rate)
This can be solved in a single line:
print sum(df.apply(lambda row: func(row[14], row[15]), axis=1))
Where func is your desired function and axis=1 ensures that the function is applied on each row as opposed to each column (which is the default).
My solution first creates a temporary series (picture: an unattached column) that is constructed by applying a function to each row in turn. The function that is actually being applied is an anonymous function indicated by the keyword lambda, which takes a single input row and which is fed a single row at a time from the apply method. That anonymous function simply calls your function func and passes the two column values in the row.
A Series can be summed using the sum function.
Note the indexing of the columns starts at 0.
Also note, saying for x in df: will iterate over the columns.
your number one problem is the following line:
for i in df_weather: This line is actually yielding you the column titles and not the rows themselves. What you're looking for is actually the following:
for i in df_weather.values():. The values will return a numpy array that you could itterate. The problem though is that the variable i will be a single row in the matrix now.
I want to use aggregate to apply some manipulations to a set of matrices, grouped by the customer_id, which is one column of my dataframe, df.
For example, I want to take the subsets of df that correspond to different customer_id's and add some columns to these subsets, and return them all.
In Python, I would use groupby and apply.
How can I do this in R?
The code I wrote looks like:
gr_TILPS = aggregate(df,by=list(df[,"customer_id"]),FUN=kmeansfunction)
Error in TILPSgroup$hour : $ operator is invalid for atomic vectors
The error is coming from the kmeansfunction I guess, which looks something like:
kmeansfunction = function(dfgroup){
Hour =dfgroup$hour
Weekday =TILPSgroup$WeekdayPrime
x <- cbind(Hour, Weekday)
colnames(x) <- c("x", "y")
(cl <- kmeans(x, 2))
clusters = cl$cluster
origclusters = as.factor(clusters)
dfgroup = cbind(dfgroup,origclusters)
return(dfgroup)
}
aggregate applies the same function to multiple single columns. If you want to work on ensembles of columns, then use this paradigm: lapply(split(df,group),function);
Try this:
gr_TILPS <- lapply( split(df, df[,"customer_id"]),
FUN=kmeansfunction)
Sounds like python might have some similarities to the experimental package: 'dplyr'. In a sense aggregate is only a column-oriented processing strategy within blocks, while the lapply(split, ), ) strategy is more applicable when you are interested in entire rows of data, defined by a blocking criterion. If you later want to row-bind those results back together you can always use do.call(rbind, res_from_lapply).