I am trying to write some Python logic to fill a csv file/pandas dataframe table called (table) with certain conditions, but I can't seem to get it to do what I want.
I have two columns in table: 1. trade_type and 2. execution_venue.
Conditional statement I want to write in Python:
The execution_venue entry will only be filled with either AQXE or AQEU, depending on the trade_type.
When the trade_type is filled with the string DARK, I want the the execution_venue to be filled with XUBS (if it was filled with AQXE before), and AQED (if it was filled with AQEU before).
Here is my code to do this:
security_mic = ('AQXE', 'AQEU')
table.loc[table['trade_type'] == 'DARK', 'execution_venue'] = {'AQXE': 'XUBS',
'AQEU': 'AQED'}.get(security_mic)
When I replace the right hand side of the equality with a string test, I am getting the same error, so I suspect the error is to do with the left hand side, in that it is not accessing the correct place in the dataframe!
Lets use replace for substitution of old values where trade_type os DARK
d = {'AQXE': 'XUBS', 'AQEU': 'AQED'}
table.loc[table['trade_type'] == 'DARK', 'execution_venue'] = table['execution_venue'].replace(d)
I was trying to do this in Python: I have multiple prefixes to query in Bigtable, but I only want the first result of each row set defined by a prefix. In essence, applying a limit of 1 for each row set, not for the entire scan.
Imagine you have the following records' row keys:
collection_1#item1#reversed_timestamp1
collection_1#item1#reversed_timestamp2
collection_1#item2#reversed_timestamp3
collection_1#item2#reversed_timestamp4
What if I want to retrieve just the latest entries for collection_1#item1# and collection_1#item2# at the same time?
The expected output should be the rows corresponding to :
collection_1#item1#reversed_timestamp1
collection_1#item2#reversed_timestamp3
Can this be done in Bigtable?
Thanks!
Is collection_1#item1#reversed_timestamp1 the rowkey or is reversed_timestamp1 actually a timestamp?
If it is not part of the rowkey you could use a filter like cells per column
https://cloud.google.com/bigtable/docs/using-filters#cells-per-column-limit e.g.
rows = table.read_rows(filter_=row_filters.CellsColumnLimitFilter(2))
or cells per row
https://cloud.google.com/bigtable/docs/using-filters#cells-per-row-limit e.g.
rows = table.read_rows(filter_=row_filters.CellsRowLimitFilter(2))
depending on how your data is laid out.
I have a huge *.csv file contains data as the example below, and had load the *.csvfile to a dataframe named as "data"
I want to select the rows that with "CHR" column equals to "1", and my code is as below
selected_row = data.loc[data['CHR'] == '1']
the result of selected_row is correct(row 0/3/6/7/10/13 are selected in the example), however, not containing all the rows with column equals to "1", I finally found selected_row contains rows with CHR=='1' till the 16384 row of data, the 16385 row (and many following rows) of data with CHR=='1' is not selected in selected_row, please advise, thanks.
Try
selected_row = data.loc[data['CHR'].isin([1, '1'])]
i think you have got your filters mixed up
to make it more easier for you
Now apply the filter to your dataframe
#try this
filter_row= data['CHR'] == '1']. #this would return a dataframe with boolean values which you can then use afterwards
```
data.loc[filter_row]
Thanks for everyone.
By the way, it is strange that if I specify data type when reading the *.csv file, the problem also disappeared, not really know the reason behind and just for anyone's reference
data = pandas.read_csv("mydata.csv",dtype={"CHR":"string"})
I have a pandas dataframe in which some rows didn't pull in correctly so that the values were pushed over into the next column over. Therefore I have a column that is mostly null, but has a few instances where there is a value that should go in the previous column. Below is an example of what it looks like.
enter image description here
I need to replace the 12345 and 45678 in the Approver column with JJones in the NeedtoDelete column.
I am not sure if a for loop, or a regular expression is the right way to go. I also came across the replace function, but I'm not sure how I would set that up in this scenario. Below is the code I have tried thus far (Q1Q2 is the df name):
for Q1Q2['Approver'] in Q1Q2:
Replacement = Q1Q2.loc[Q1Q2['Need to Delete'].notnull()]
Q1Q2.loc[Replacement] = Q1Q2['Approver']
Q1Q2.loc[Q1Q2['Need to Delete'].notnull(), ['Approver'] == Q1Q2['Need to Delete']]
If you could help me fix either attempts above, or point me in the right direction, it would be greatly appreciated. Thanks in advance!
You can use boolean indexing:
r=Q1Q2['Need to Delete'].notnull()
Q1Q2.loc[r,'Approver']=Q1Q2.loc[r,'Need to Delete']
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