I work with a test system that outputs a large CSV matrix of values which I then process using the Pandas module in Python. The parameters that system uses when testing a given part are governed by a predetermined sequence. A simplified example is shown here:
Raw data frame
However, not all of these steps are desired in the output data. In fact, the rows containing a 'Clock Frequency' value of '3.0MHz' are only included to act as buffer points to allow a climate chamber to reach the intended temperature. I do not wish to include data collected at these parameters in my results.
I found I was pretty easily able to remove these rows from my data frame by using the below code. Note that in this example I am working with a Pandas data frame called 'csvDF'.
tempBuffers = csvDF[csvDF['Clock Frequency']==3e6].index
csvDF.drop(tempBuffers, inplace=True)
This produces the following output:
Data frame with buffer steps removed
The issue with this is that now my 'Sequence Step' column is wrong. I want the data table to appear as if those buffer steps never existed. The sequence steps should be sequential for all non-buffer steps. The desired output is shown below:
Data frame with buffer steps removed and corrected sequence step column
What code do I need to instantiate in order to achieve this?
You can try something like this:
n = 3 # number of rows in step
csvDF.reset_index(inplace=True, drop=True)
csvDF['Sequence step'] = pd.Series(range(len(csvDF)))
csvDF['Sequence step'] = csvDF['Sequence step'].apply(lambda x: int(x / n))
I have a DF loaded with orders. Some of them contains negative quantities, and the reason for that is that they are actually cancellations of prior orders.
Problem, there is no unique key that can help me find back which order corresponds to which cancellation.
So I've built the following code ('cancelations' is a subset of the original data containing only the rows that correspond to... well... cancelations):
for i, item in cancelations.iterrows():
#find a row similar to the cancelation we are currently studying:
#We use item[1] to access second value of the tuple given back by iterrows()
mask1 = (copy['CustomerID'] == item['CustomerID'])
mask2 = (copy['Quantity'] == item['Quantity'])
mask3 = (copy['Description'] == item['Description'])
subset = copy[ mask1 & mask2 & mask3]
if subset.shape[0] >0: #if we find one or several corresponding orders :
print('possible corresponding orders:', subset.index.tolist())
copy = copy.drop(subset.index.tolist()[0]) #retrieve only the first ot them from the copy of the data
So, this works, but :
first, it takes forever to run; and second, I read somewhere that whenever you find yourself writing complex code to manipulate dataframes, there's already a method for it.
So perhaps one of you know something that could help me ?
thank you for your time !
edit : note that sometimes, we can have several orders that could correspond to the cancelation at hand. This is why I didn't use drop_duplicates with only some columns specified... because it eliminates all duplicates (or all but one) : I need to drop only one of them.
I changed this post due to lack of response and asking to broad of a question on my part, I think.I have taken the original csv and grouped/ selected out all the records that are the latest and placed them in a new df as well as created another df without the latest records.
I have two data frames, one with a the latest version of that record:
new_df = [{'ful_id':000c1a6c-1f1c, 'version':3, 'xs':123, 'at_grade':yes, 'date':20171003},
{'ful_id':00dc5fec-ddb8, 'version':2, 'xs':556, 'at_grade': , 'date':20171009}]
and another with older versions of that record:
old = [{'ful_id':000c1a6c-1f1c, 'version':2, 'xs': , 'at_grade':yes, 'date':20170902},
{'ful_id':000c1a6c-1f1c, 'version':1, 'xs': , 'at_grade':yes, 'date':20170810},
{'ful_id':00dc5fec-ddb8, 'version':1, 'xs':556, 'at_grade':no, 'date':20170803}]
*Data example though the real spread sheet has 130 columns and 20k plus records
I need to run through each record compare ids and then loop through all versions of that id and see if data was deleted in the new version that was in the old. I do not care about other changes, ex if the new version contains data that the old did not. So I was thinking about doing a Boolean comparison? The output would be a any record id missing info along with the column that was changed.
import pandas as pd
import numpy as np
#empty table for comparison
compare = []
#now im not sure how to proceed
for i,r in new_df.iterrows():
if (current['ful_id']== old_v['ful_id']):
turn values into boolean and compare any([])
if value is false in new version but true in old
compare.append
else :
continue through next id group
The last part is not code I know, im just unsure how to proceed
For my output I would like a csv with the id and columns that that are different then the current version. So for the example above, only one record and one column would be in the output due to the current version having no at_grade value.
ful_id at_grade
00dc5fec-ddb8 false
I'm trying to use panda to do some analysis on some messaging data and am running into a few problems try to prep the data. It is coming from a database I don't have control of and therefore I need to do a little pruning and formatting before analyzing it.
Here is where I'm at so far:
#select all the messages in the database. Be careful if you get the whole test data base, may have 5000000 messages.
full_set_data = pd.read_sql("Select * from message",con=engine)
After I make this change to the timestamp, and set it as index, I'm no longer and to call to_csv.
#convert timestamp to a timedelta and set as index
#full_set_data[['timestamp']] = full_set_data[['timestamp']].astype(np.timedelta64)
indexed = full_set_data.set_index('timestamp')
indexed.to_csv('indexed.csv')
#extract the data columns I really care about since there as a bunch I don't need
datacolumns = indexed[['address','subaddress','rx_or_tx', 'wordcount'] + [col for col in indexed.columns if ('DATA' in col)]]
Here I need to format the DATA columns, I get a "SettingWithCopyWarning".
#now need to format the DATA columns to something useful by removing the upper 4 bytes
for col in datacolumns.columns:
if 'DATA' in col:
datacolumns[col] = datacolumns[col].apply(lambda x : int(x,16) & 0x0000ffff)
datacolumns.to_csv('data_col.csv')
#now group the data by "interaction key"
groups = datacolumns.groupby(['address','subaddress','rx_or_tx'])
I need to figure out how to get all the messages from a given group. get_group() requires I know key values ahead of time.
key_group = groups.get_group((1,1,1))
#foreach group in groups:
#do analysis
I have tried everything I could think of to fix the problems I'm running into but I cant seem to get around it. I'm sure it's from me misunderstanding/misusing Pandas as I'm still figuring it out.
I looking to solve these issues:
1) Can't save to csv after I add index of timestamp as timedelta64
2) How do I apply a function to a set of columns to remove SettingWithCopyWarning when reformatting DATA columns.
3) How to grab the rows for each group without having to use get_group() since I don't know the keys ahead of time.
Thanks for any insight and help so I can better understand how to properly use Pandas.
Firstly, you can set the index column(s) and parse dates while querying the DB:
indexed = pd.read_sql_query("Select * from message", engine=engine,
parse_dates='timestamp', index_col='timestamp')
Note I've used pd.read_sql_query here rather than pd.read_sql, which is deprecated, I think.
SettingWithCopy warning is due to the fact that datacolumns is a view of indexed, i.e. a subset of it's rows /columns, not an object in it's own right. Check out this part of the docs: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
One way to get around this is to define
datacolumns = indexed[<cols>].copy()
Another would to do
indexed = indexed[<cols>]
which effectively removes the columns you don't want, if you're happy that you won't need them again. You can then manipulate indexed at your leisure.
As for the groupby, you could introduce a columns of tuples which would be the group keys:
indexed['interaction_key'] = zip(indexed[['address','subaddress','rx_or_tx']]
indexed.groupby('interaction_key').apply(
lambda df: some_function(df.interaction_key, ...)
I'm not sure if it's all exactly what you want but let me know and I can edit.
I'm having trouble with processing some csv data files for a project. Someone suggested using python/csv reader to help break down the files, which I've had some success with, but not in a way I can use.
This code is a little different from what I was trying before. I am essentially attempting to create an array. In the raw data format, the first 7 rows contain no data, and then each column contains 50 experiments, each with 4000 rows, for 200000 some rows total. What I want to do is take each column, and make it an individual csv file, with each experiment in its own column. So it would be an array of 50 columns and 4000 rows for each data type. The code here does break down the correct values, I think the logic is okay, but it is breaking down the opposite of how I want it. I want the separators without quotes (the commas and spaces) and I want the element values in quotes. Right now it is doing just the opposite for both, element values with no quotes, and the separators in quotes. I've spent several hours trying to figure out how to do this to no avail,
import csv
ifile = open('00_follow_maverick.csv')
epistemicfile = open('00_follower_maverick_EP.csv', 'w')
reader = csv.reader(ifile)
colnum = 0
rownum = 0
y = 0
z = 8
for column in reader:
rownum = 4000 * y + z
for element in column:
writer = csv.writer(epistemicfile)
if y <= 50:
y = y + 1
writer.writerow([element])
writer.writerow(',')
rownum = x * y + z
if y > 50:
y = 0
z = z + 1
writer.writerow(' ')
rownum = x * y + z
if z >= 4008:
break
What is going on: I am taking each row in the raw data file in iterations of 4000, so that I can separate them with commas for the 50 experiments. When y, the experiment indicator here, reaches 50, it resets back to experiment 0, and adds 1 to z, which tells it which row to look at, by the formula of 4000 * y + z. When it completes the rows for all 50 experiments, it is finished. The problem here is that I don't know how to get python to write the actual values in quotes, and my separators outside of quotes.
Any help will be most appreciated. Apologies if this seems a stupid question, I have no programming experience, this is my first attempt ever. Thank you.
Sorry, I'll try to make this more clear. The original csv file has several columns, each of which are different sets of data.
A miniature example of the raw file looks like:
column1 column2 column3
exp1data1time1 exp1data2time1 exp1data3time1
exp1data1time2 exp1data2time2 exp1data3time2
exp2data1time1 exp2data2time1 exp2data3time1
exp2data1time2 exp2data2time2 exp2data3time2
exp3data1time1 exp3data2time1 exp3data3time1
exp3data1time2 exp3data2time2 exp3data3time2
So, the actual version has 4000 rows instead of 2 for each new experiment. There are 40 columns in the actual version, but basically, the data type in the raw file matches the column number. I want to separate each data type or column into an individual csv file.
This would look like:
csv file1
exp1data1time1 exp2data1time1 exp3data1time1
exp1data1time2 exp2data1time2 exp3data1time2
csv file2
exp1data2time1 exp2data2time1 exp3data2time1
exp1data2time2 exp2data2time2 exp3data2time2
csv file3
exp1data3time1 exp2data3time1 exp3data3time1
exp1data3time2 exp2data3time2 exp3data3time2
So, I'd move the raw data in the file to a new column, and each data type to its own file. Right now I'm only going to do one file, until I can move the separate experiments to separate columns in the new file. So, in the code, the above would make the 4000 into 2. I hope this makes more sense, but if not, I will try again.
If I had a cat for each time I saw a bio or psych or chem database in this state:
"each column contains 50 experiments,
each with 4000 rows, for 200000 some
rows total. What I want to do is take
each column, and make it an individual
csv file, with each experiment in its
own column. So it would be an array of
50 columns and 4000 rows for each data
type"
I'd have way too farking many cats.
I didn't even look at your code because the re-mangling you are proposing is just another problem that will have to be solved. I don't fault you, you claim to be a novice and all your peers make the same sort of error. Beginning programmers who have yet to understand how to use arrays often wind up with variable declarations like:
integer response01, response02, response03, response04, ...
and then very, very redundant code when they try to see if every response is - say - 1. I think this is such a seductive error in bio-informatics because it actually models the paper notations they come from rather well. Unfortunately, the sheet-of-paper model isn't the best way to model data.
You should read and understand why database normalization was developed, codified and has come to dominate how people think about structured data. One Wikipedia article may not be sufficient. Using the example I excerpted let me try to explain how I think of it. Your data consists of observations; put the other way the primary datum is a singular observation. That observation has a context though: it is one of a set of 4000 observations, where each set belongs to one of 50 experiments. If you had to attach a context to each observation you'd wind up with an addressing scheme that looks like:
<experiment_number, observation_number, value>
In database jargon, that's a tuple, and it is capable of representing, with no ambiguity and perfect symmetry the entirety of your data. I'm not certain that I've understood the exact structure of your data, so perhaps it is something more like:
<experiment_number, protocol_number, observation_number, value>
where the protocol may be some form of variable treatment type - let's say pH. But note that I didn't call the protocol a pH and I don't record it as such in the database. What I would then need is an ancillary table showing the relevant parameters of the protocol, e.g.:
<protocol_number, acidity, temperature, pressure>
Now we've just built a "relation" that those database people like to talk about; we've also begun normalizing the data. If you need to know the pH for a given protocol, there is one and only one place to find it, in the proper row of the protocol table. Note that I've divorced the data that fit so nicely together on a data-sheet and from the observation table I can't see the pH for a particular dataum. But that's okay, because I can just look it up in my protocol table if needed. This is a "relational join" and if I needed to, I could coalesce all the various parameters from all the various tables and reconstitute the original datasheet in its original, unstructured glory.
I hope this answer is of some use to you. I'm certain that I don't even know what field of study your data is from, but these principles apply across domains from drug trials to purchase requisition processing. Please understand that I'm trying to inform, per your request, and there is zero condescension intended. I welcome further questions on the matter.
Normalization of the dataset
Thanks for giving the example. You have the context I described already, perhaps I can make it more clear.
column1 column2 column3
exp1data1time1 exp1data2time1 exp1data3time1
exp1data1time2 exp1data2time2 exp1data3time2
The columns are an artifice made by the last guy; that is, they carry no relevant information. When parsed into a normal form, your data looks just like my first proposed tuple:
<experiment_number, time, response_number, response>
where I suspect time may actually mean "subject_id" or "trial_number". It may very well look incongruous to you to conjoin all the different response values into the same dataset; indeed based on your desired output, I suspect that it does. At first blush, the objection "but the subject's response to a question about epistemic properties of chairs has no connection to their meta-epistemic beliefs regarding color", but this would be mistaken. The data are related because they have a common experimental subject, and self-correlation is an important concept in sociological analytics.
For example, you may find that respondent A gives the same responses as respondent B, except all of A's responses are biased one higher because of how the subject understood the criteria. This would make a very real difference in the absolute values of the data, but I hope you can see that the question "do A and B actually have different epistemic models?" is salient and valid. One method of data modeling allows this question to be answered easily, your desired method does not.
Working parsing code to follow shortly.
The normalizing code
#!/usr/bin/python
"""parses a csv file containing a particular data layout and normalizes
The raw data set is a csv file of the form::
column1 column2 column3
exp01data01time01 exp01data02time01 exp01data03time01
exp01data01time02 exp01data02time02 exp01data03time02
where there are 40 such columns and the literal column title
is added as context to the output row
it is assumed that the columns are comma separated but
the lexical form of the subcolumns is unspecified.
Output will consist of a single CSV output stream
on stdout of the form::
exp01, time01, data01, column1
for varying actual values of each field.
"""
import csv
import sys
def split_subfields(s):
"""returns a list of subfields of s
this function is expected to be re-written to match the actual,
unspecified lexical structure of s."""
return [s[0:5], s[5:11], s[11:17]]
def normalise_data(reader, writer):
"""returns a list of the column headings from the reader"""
# obtain the headings for use in normalization
names = reader.next()
# get the data rows, split them out by column, add the column name
for row in reader:
for column, datum in enumerate(row):
fields = split_subfields(datum)
fields.append(names[column])
writer.writerow(fields)
def main():
if len(sys.argv) != 2:
print >> sys.stderr, ('usage: %s input.csv' % sys.argv[0])
sys.exit(1)
in_file = sys.argv[1]
reader = csv.reader(open(in_file))
writer = csv.writer(sys.stdout)
normalise_data(reader, writer)
if __name__ == '__main__': main()
Such that the command python epistem.py raw_data.csv > cooked_data.csv yields excerpted output looking like:
exp01,data01,time01,column1
...
exp01,data40,time01,column40
exp01,data01,time02,column1
exp01,data01,time03,column1
...
exp02,data40,time15,column40