Develop Reference

Add Function to new Column by Row Value using Vectorization

I have a Pandas DataFrame with many columns and I need to take the part number column and use the data in it to populate the features column. My function add_data takes the part number and looks it up in a SQL database and returns the feature notes.
I have this working with df.apply and it works well
(code block 1).
Because I recently started learning vectorization and was wondering if there was a way to do this without df.apply(code block 2)
#Working Code
df['Features'] = df.apply (lambda row: add_data(row['partnumber']), axis=1)
def add_data(row):
featurenotes = sql_lookup(row)
return featurenotes
This line of code calls my add_data function but I don't know how to grab just the value of the row in the part number column to use in my function.
df['Features'] = add_data(df['partnumber'])
I'm am very new to python so I am trying to learn best practices and how to manipulate pandas DataFrames.

Pyspark: auto-increment starting from specific value

I need to add a column to my dataframe that would increment by 1 but starting from 500. So the first row would be 500, the second one 501 etc. It doesn't make sense to use UDF, since it can be executed on a different workers and I don't know any function that would take starting value as a parameter. I don't have anything that I could sort my dataframe on either. Both row number and auto increment would start on 1 by default. I believe I can do it in but transforming my df to rdd and back to df seems to be quite ugly solution. Do you know of any existing function that would help me to solve in on a dataframe level?
Thank you!

Since monotonically_increasing_id() isn't consecutive, you can use row_num() over monotonically_increasing_id() and add 499.
from pyspark.sql.window import Window
df = df.withColumn("idx", monotonically_increasing_id())
w = Window().orderBy("idx")
df.withColumn("row_num", (499 + row_number().over(w))).show()

I think you can use monotonically_increasing_id function which starts from 0, but you can start from a custom offset by adding a constant value to each offset:
offset = start_offset + monotonically_increasing_id()

Defining a function with args to be used in df.transform

For a current project, I am planning to winsorize a Pandas DataFrame that consists of two columns/objects df['Policies'] and df['ProCon']. This means that the outliers at the high and the low end of the set shall be cut out.
The winsorising shall be conducted at 0.05 and 0.95 based on the values shown in the df['ProCon'] section, while both columns shall be cut out in case an outlier is identified.
The code below is however not accepting the direct reference to the 'ProCon' column in line def winsorize_series(df['ProCon']):, yielding an error about an invalid syntax.
Is there any smart way to indicate that ProCon shall be the determining value for the winsorizing?
import pandas as pd
from scipy.stats import mstats
# Loading the file
df = pd.read_csv("3d201602.csv")
# Winsorizing
def winsorize_series(df['ProCon']):
return mstats.winsorize(df['ProCon'], limits=[0.05,0.95])
# Defining the winsorized DataFrame
df = df.transform(winsorize_series)

Have you tried separating the column name from the table?
def winsorize_series(df, column):
return mstats.winsorize(df[column], limits=[0.05,0.95])
Can't test it though if there's no sample data.

As per comments, .transform is not the right choice to modify only one or selected columns from df. Whatever the function definition and arguments passed, transform will iterate and pass EVERY column to func and try to broadcast the joined result to the original shape of df.
What you need is much simpler
limits = [0.05,0.95] # keep limits static for any calls you make
colname = 'ProCon' # you could even have a list of columns and loop... for colname in cols
df[colname] = mstats.winsorize(df[colname], limits=limits)
df.transform(func) can be passed *args and **kwargs which will be passed to func, as in
df = df.transform(mstats.winsorize, axis=0, a=df['ProCon'], limits=[0.05,0.95])
So there is no need for
def winsorize_series...

Returning a dataframe in Dask

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.

Python Dataframe for loop syntax

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.