Python – Detect and exclude outliers in Pandas data frame

dataframefilteringoutlierspandaspython

I have a pandas data frame with few columns.

Now I know that certain rows are outliers based on a certain column value.

For instance

column 'Vol' has all values around 12xx and one value is 4000 (outlier).

Now I would like to exclude those rows that have Vol column like this.

So, essentially I need to put a filter on the data frame such that we select all rows where the values of a certain column are within, say, 3 standard deviations from mean.

What is an elegant way to achieve this?

Best Answer

If you have multiple columns in your dataframe and would like to remove all rows that have outliers in at least one column, the following expression would do that in one shot.

df = pd.DataFrame(np.random.randn(100, 3))

from scipy import stats
df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]

description:

For each column, it first computes the Z-score of each value in the column, relative to the column mean and standard deviation.
It then takes the absolute Z-score because the direction does not matter, only if it is below the threshold.
all(axis=1) ensures that for each row, all column satisfy the constraint.
Finally, the result of this condition is used to index the dataframe.

Filter other columns based on a single column

Specify a column for the zscore, df[0] for example, and remove .all(axis=1).

df[(np.abs(stats.zscore(df[0])) < 3)]

Related Solutions

R – Drop data frame columns by name

You can use a simple list of names :

DF <- data.frame(
  x=1:10,
  y=10:1,
  z=rep(5,10),
  a=11:20
)
drops <- c("x","z")
DF[ , !(names(DF) %in% drops)]

Or, alternatively, you can make a list of those to keep and refer to them by name :

keeps <- c("y", "a")
DF[keeps]

EDIT : For those still not acquainted with the drop argument of the indexing function, if you want to keep one column as a data frame, you do:

keeps <- "y"
DF[ , keeps, drop = FALSE]

drop=TRUE (or not mentioning it) will drop unnecessary dimensions, and hence return a vector with the values of column y.

Python – “Large data” workflows using pandas

I routinely use tens of gigabytes of data in just this fashion e.g. I have tables on disk that I read via queries, create data and append back.

It's worth reading the docs and late in this thread for several suggestions for how to store your data.

Details which will affect how you store your data, like:
Give as much detail as you can; and I can help you develop a structure.

Size of data, # of rows, columns, types of columns; are you appending rows, or just columns?
What will typical operations look like. E.g. do a query on columns to select a bunch of rows and specific columns, then do an operation (in-memory), create new columns, save these.
(Giving a toy example could enable us to offer more specific recommendations.)
After that processing, then what do you do? Is step 2 ad hoc, or repeatable?
Input flat files: how many, rough total size in Gb. How are these organized e.g. by records? Does each one contains different fields, or do they have some records per file with all of the fields in each file?
Do you ever select subsets of rows (records) based on criteria (e.g. select the rows with field A > 5)? and then do something, or do you just select fields A, B, C with all of the records (and then do something)?
Do you 'work on' all of your columns (in groups), or are there a good proportion that you may only use for reports (e.g. you want to keep the data around, but don't need to pull in that column explicity until final results time)?

Solution

Ensure you have pandas at least 0.10.1 installed.

Read iterating files chunk-by-chunk and multiple table queries.

Since pytables is optimized to operate on row-wise (which is what you query on), we will create a table for each group of fields. This way it's easy to select a small group of fields (which will work with a big table, but it's more efficient to do it this way... I think I may be able to fix this limitation in the future... this is more intuitive anyhow):
(The following is pseudocode.)

import numpy as np
import pandas as pd

# create a store
store = pd.HDFStore('mystore.h5')

# this is the key to your storage:
#    this maps your fields to a specific group, and defines 
#    what you want to have as data_columns.
#    you might want to create a nice class wrapping this
#    (as you will want to have this map and its inversion)  
group_map = dict(
    A = dict(fields = ['field_1','field_2',.....], dc = ['field_1',....,'field_5']),
    B = dict(fields = ['field_10',......        ], dc = ['field_10']),
    .....
    REPORTING_ONLY = dict(fields = ['field_1000','field_1001',...], dc = []),

)

group_map_inverted = dict()
for g, v in group_map.items():
    group_map_inverted.update(dict([ (f,g) for f in v['fields'] ]))

Reading in the files and creating the storage (essentially doing what append_to_multiple does):

for f in files:
   # read in the file, additional options may be necessary here
   # the chunksize is not strictly necessary, you may be able to slurp each 
   # file into memory in which case just eliminate this part of the loop 
   # (you can also change chunksize if necessary)
   for chunk in pd.read_table(f, chunksize=50000):
       # we are going to append to each table by group
       # we are not going to create indexes at this time
       # but we *ARE* going to create (some) data_columns

       # figure out the field groupings
       for g, v in group_map.items():
             # create the frame for this group
             frame = chunk.reindex(columns = v['fields'], copy = False)    

             # append it
             store.append(g, frame, index=False, data_columns = v['dc'])

Now you have all of the tables in the file (actually you could store them in separate files if you wish, you would prob have to add the filename to the group_map, but probably this isn't necessary).

This is how you get columns and create new ones:

frame = store.select(group_that_I_want)
# you can optionally specify:
# columns = a list of the columns IN THAT GROUP (if you wanted to
#     select only say 3 out of the 20 columns in this sub-table)
# and a where clause if you want a subset of the rows

# do calculations on this frame
new_frame = cool_function_on_frame(frame)

# to 'add columns', create a new group (you probably want to
# limit the columns in this new_group to be only NEW ones
# (e.g. so you don't overlap from the other tables)
# add this info to the group_map
store.append(new_group, new_frame.reindex(columns = new_columns_created, copy = False), data_columns = new_columns_created)

When you are ready for post_processing:

# This may be a bit tricky; and depends what you are actually doing.
# I may need to modify this function to be a bit more general:
report_data = store.select_as_multiple([groups_1,groups_2,.....], where =['field_1>0', 'field_1000=foo'], selector = group_1)

About data_columns, you don't actually need to define ANY data_columns; they allow you to sub-select rows based on the column. E.g. something like:

store.select(group, where = ['field_1000=foo', 'field_1001>0'])

They may be most interesting to you in the final report generation stage (essentially a data column is segregated from other columns, which might impact efficiency somewhat if you define a lot).

You also might want to:

create a function which takes a list of fields, looks up the groups in the groups_map, then selects these and concatenates the results so you get the resulting frame (this is essentially what select_as_multiple does). This way the structure would be pretty transparent to you.
indexes on certain data columns (makes row-subsetting much faster).
enable compression.

Let me know when you have questions!