I'm fairly new to python and I'm looking forward to achieve the following:
I have a table with several conditions as in the image below (maximum 5 conditions) along with various attributes. Each condition comes from a specific set of values, for example Condition 1 has 2 possible values, Condition 2 has 4 possible values, Condition 3 has 2 possible values etc..
What I would like to do: From the example table above, I would like to generate a simple python code so that when I execute my function and import a CSV file containing the table above, I should get the following output saved as a *.py file:
def myFunction(Attribute, Condition):
if Attribute1 & Condition1:
myValue = val_11
if Attribute1 & Condition2:
myValue = val_12
...
...
if Attribute5 & Condition4:
myValue = val_54
NOTE: Each CSV file will contain only one sheet and the titles for the columns do not change.
UPDATE, NOTE#2: Both "Attribute" and "Condition" are string values, so simple string comparisons would suffice.
Is there a simple way to do this? I dove into NLP and realized that it is not possible (at least from what I found in the literature). I'm open to all forms of suggestions/answers.
You can't really use "If"s and "else"s, since, if I understand your question correctly, you want to be able to read the conditions, attributes and values from a CSV file. Using "If"s and "else"s, you would only be able to check a fixed range of conditions and attributes defined in your code. What I would do, is to write a parser (piece of code, which reads the contents of your CSV file and saves it in another, more usable form).
In this case, the parser is the parseCSVFile() function. Instead of the ifs and elses comparing attributes and conditions, you now use the attributes and conditions to access a specific element in a dictionary (similar to an array or list, but you can now use for example string keys instead of the numerical indexes). I used a dictionary containing a dictionary at each position to split the CSV contents into their rows and columns. Since I used dictionaries, you can now use the strings of the Attributes and Conditions to access your values instead of doing lots of comparisons.
#Output Dictionary
ParsedDict = dict()
#This is either ';' or ',' depending on your operating system or you can open a CSV file with notepad for example to check which character is used
CSVSeparator = ';'
def parseCSVFile(filePath):
global ParsedDict
f = open(filePath)
fileLines = f.readlines()
f.close()
#Extract the conditions
ConditionsArray = (fileLines[0].split(CSVSeparator))[1:]
for x in range(len(fileLines)-1):
#Remove unwanted characters such as newline characters
line = fileLines[1 + x].strip()
#Split by the CSV separation character
LineContents = line.split(CSVSeparator)
ConditionsDict = dict()
for y in range(len(ConditionsArray)):
ConditionsDict.update({ConditionsArray[y]: LineContents[1 + y]})
ParsedDict.update({LineContents[0]: ConditionsDict})
def myFunction(Attribute, Condition):
myValue = ParsedDict[Attribute][Condition]
The "[1:]" is to ignore the contents in the first column (empty field at the top left and the "Attribute x" fields) when reading either the conditions or the values
Use the parseCSVFile() function to extract the information from the csv file
and the myFunction() to get the value you want
Re-learning python after not using it for a few years - so go nice on me.
The basis, is I am reading in data from a .csv file, the information i am reading in is as follows
E1435
E46371
E1696
E27454
However, when using print(list[0]) for example, it produces
['E1435']
I am trying to use these pieces of data to interpolate into an API request string, and the " [' '] " in them is breaking the requests - basically, I need the elements in the list to not have the square brackets and quotes when using them as variables.
My interpolation is as follows, in case the way I'm interpolating is the problem:
req = requests.get('Linkgoeshere/%s' % list[i])
Edit;
A sample of the data i'm using is listed above with "E1435, E46371" etc. each item in the csv is a new row in the same column.
As per a request, i have produced a minimal reproduction of my experience.
import csv
#list to store data from csv
geoCode = []
#Read in locations from a designated file
with open('Locations.csv','rt')as f:
data = csv.reader(f)
for row in data:
geoCode.append(row)
i=0
for item in geoCode:
#print the items in the list
print(geoCode[i])
i+=1
It appears that list[i] is itself a nested list, so you need another subscript to get to the element inside it:
print(list[i][0])
NB: Avoid naming variables list as it overrides the built-in list type. Try using a plural word like codes or ids instead.
I'm reading a CSV file using csv.Dicreader. It returns only the last line as a dict but I want to return all of the lines.
I'm filtering the entire row file with dictionary comprehension to get only two keys:value using the field dict, then doing a little cleanup. I need to return each line as a dict after the cleaning process. Finally, it should return a dict.
for row in reader:
data={value:row[key] for key, value in fields.items()}
if data['binomialAuthority']=='NULL':
data['binomialAuthority']=None
data['label']=re.sub(r'\(.*?\)','',data['label']).strip()
return data
out put:
data= {{'label': 'Argiope', 'binomialAuthority': None}
{'label': 'Tick', 'binomialAuthority': None}}
Each iteration through the loop, you assign to data a single value. Think of data like a small markerboard that only has the last thing you wrote on it. At the end of the loop it will refer to the last item assigned.
If you just want to print your structure, move the print statement into the loop.
If you want a data structure containing multiple dicts, then you need to create a list and then append to it in the loop. Note that this will use a lot of memory when loading a large file.
eg.
my_list = []
for row in reader:
data = '...'
my_list.append(data)
return my_list
the best way is to append it to a list and then use a for loop to unwind the list so that you get a dict type.
my_list = []
for row in reader:
data = '...'
my_list.append(data)
for i in my_list:
print (i)
I am reading a text file with python, formatted where the values in each column may be numeric or strings.
When those values are strings, I need to assign a unique ID of that string (unique across all the strings under the same column; the same ID must be assigned if the same string appears elsewhere under the same column).
What would be an efficient way to do it?
Use a defaultdict with a default value factory that generates new ids:
ids = collections.defaultdict(itertools.count().next)
ids['a'] # 0
ids['b'] # 1
ids['a'] # 0
When you look up a key in a defaultdict, if it's not already present, the defaultdict calls a user-provided default value factory to get the value and stores it before returning it.
collections.count() creates an iterator that counts up from 0, so collections.count().next is a bound method that produces a new integer whenever you call it.
Combined, these tools produce a dict that returns a new integer whenever you look up something you've never looked up before.
defaultdict answer updated for python 3, where .next is now .__next__, and for pylint compliance, where using "magic" __*__ methods is discouraged:
ids = collections.defaultdict(functoools.partial(next, itertools.count()))
Create a set, and then add strings to the set. This will ensure that strings are not duplicated; then you can use enumerate to get a unique id of each string. Use this ID when you are writing the file out again.
Here I am assuming the second column is the one you want to scan for text or integers.
seen = set()
with open('somefile.txt') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
try:
int(row[1])
except ValueError:
seen.add(row[1]) # adds string to set
# print the unique ids for each string
for id,text in enumerate(seen):
print("{}: {}".format(id, text))
Now you can take the same logic, and replicate it across each column of your file. If you know the column length in advanced, you can have a list of sets. Suppose the file has three columns:
unique_strings = [set(), set(), set()]
with open('file.txt') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
for column,value in enumerate(row):
try:
int(value)
except ValueError:
# It is not an integer, so it must be
# a string
unique_strings[column].add(value)
I am writing a script that looks through my inventory, compares it with a master list of all possible inventory items, and tells me what items I am missing. My goal is a .csv file where the first column contains a unique key integer and then the remaining several columns would have data related to that key. For example, a three row snippet of my end-goal .csv file might look like this:
100001,apple,fruit,medium,12,red
100002,carrot,vegetable,medium,10,orange
100005,radish,vegetable,small,10,red
The data for this is being drawn from a couple sources. 1st, a query to an API server gives me a list of keys for items that are in inventory. 2nd, I read in a .csv file into a dict that matches keys with item name for all possible keys. A snippet of the first 5 rows of this .csv file might look like this:
100001,apple
100002,carrot
100003,pear
100004,banana
100005,radish
Note how any key in my list of inventory will be found in this two column .csv file that gives all keys and their corresponding item name and this list minus my inventory on hand yields what I'm looking for (which is the inventory I need to get).
So far I can get a .csv file that contains just the keys and item names for the items that I don't have in inventory. Give a list of inventory on hand like this:
100003,100004
A snippet of my resulting .csv file looks like this:
100001,apple
100002,carrot
100005,radish
This means that I have pear and banana in inventory (so they are not in this .csv file.)
To get this I have a function to get an item name when given an item id that looks like this:
def getNames(id_to_name, ids):
return [id_to_name[id] for id in ids]
Then a function which gives a list of keys as integers from my inventory server API call that returns a list and I've run this function like this:
invlist = ServerApiCallFunction(AppropriateInfo)
A third function takes this invlist as its input and returns a dict of keys (the item id) and names for the items I don't have. It also writes the information of this dict to a .csv file. I am using the set1 - set2 method to do this. It looks like this:
def InventoryNumbers(inventory):
with open(csvfile,'w') as c:
c.write('InvName' + ',InvID' + '\n')
missinginvnames = []
with open("KeyAndItemNameTwoColumns.csv","rb") as fp:
reader = csv.reader(fp, skipinitialspace=True)
fp.readline() # skip header
invidsandnames = {int(id): str.upper(name) for id, name in reader}
invids = set(invidsandnames.keys())
invnames = set(invidsandnames.values())
invonhandset = set(inventory)
missinginvidsset = invids - invonhandset
missinginvids = list(missinginvidsset)
missinginvnames = getNames(invidsandnames, missinginvids)
missinginvnameswithids = dict(zip(missinginvnames, missinginvids))
print missinginvnameswithids
with open(csvfile,'a') as c:
for invname, invid in missinginvnameswithids.iteritems():
c.write(invname + ',' + str(invid) + '\n')
return missinginvnameswithids
Which I then call like this:
InventoryNumbers(invlist)
With that explanation, now on to my question here. I want to expand the data in this output .csv file by adding in additional columns. The data for this would be drawn from another .csv file, a snippet of which would look like this:
100001,fruit,medium,12,red
100002,vegetable,medium,10,orange
100003,fruit,medium,14,green
100004,fruit,medium,12,yellow
100005,vegetable,small,10,red
Note how this does not contain the item name (so I have to pull that from a different .csv file that just has the two columns of key and item name) but it does use the same keys. I am looking for a way to bring in this extra information so that my final .csv file will not just tell me the keys (which are item ids) and item names for the items I don't have in stock but it will also have columns for type, size, number, and color.
One option I've looked at is the defaultdict piece from collections, but I'm not sure if this is the best way to go about what I want to do. If I did use this method I'm not sure exactly how I'd call it to achieve my desired result. If some other method would be easier I'm certainly willing to try that, too.
How can I take my dict of keys and corresponding item names for items that I don't have in inventory and add to it this extra information in such a way that I could output it all to a .csv file?
EDIT: As I typed this up it occurred to me that I might make things easier on myself by creating a new single .csv file that would have date in the form key,item name,type,size,number,color (basically just copying in the column for item name into the .csv that already has the other information for each key.) This way I would only need to draw from one .csv file rather than from two. Even if I did this, though, how would I go about making my desired .csv file based on only those keys for items not in inventory?
ANSWER: I posted another question here about how to implement the solution I accepted (becauseit was giving me a value error since my dict values were strings rather than sets to start with) and I ended up deciding that I wanted a list rather than a set (to preserve the order.) I also ended up adding the column with item names to my .csv file that had all the other data so that I only had to draw from one .csv file. That said, here is what this section of code now looks like:
MyDict = {}
infile = open('FileWithAllTheData.csv', 'r')
for line in infile.readlines():
spl_line = line.split(',')
if int(spl_line[0]) in missinginvids: #note that this is the list I was using as the keys for my dict which I was zipping together with a corresponding list of item names to make my dict before.
MyDict.setdefault(int(spl_line[0]), list()).append(spl_line[1:])
print MyDict
it sounds like what you need is a dict mapping ints to sets, ie,
MyDict = {100001: set([apple]), 100002: set([carrot])}
you can add with update:
MyDict[100001].update([fruit])
which would give you: {100001: set([apple, fruit]), 100002: set([carrot])}
Also if you had a list of attributes of carrot... [vegetable,orange]
you could say MyDict[100002].update([vegetable, orange])
and get: {100001: set([apple, fruit]), 100002: set([carrot, vegetable, orange])}
does this answer your question?
EDIT:
to read into CSV...
infile = open('MyFile.csv', 'r')
for line in infile.readlines():
spl_line = line.split(',')
if int(spl_line[0]) in MyDict.keys():
MyDict[spl_line[0]].update(spl_line[1:])
This isn't an answer to the question, but here is a possible way of simplifying your current code.
This:
invids = set(invidsandnames.keys())
invnames = set(invidsandnames.values())
invonhandset = set(inventory)
missinginvidsset = invids - invonhandset
missinginvids = list(missinginvidsset)
missinginvnames = getNames(invidsandnames, missinginvids)
missinginvnameswithids = dict(zip(missinginvnames, missinginvids))
Can be replaced with:
invonhandset = set(inventory)
missinginvnameswithids = {k: v for k, v in invidsandnames.iteritems() if k in in inventory}
Or:
invonhandset = set(inventory)
for key in invidsandnames.keys():
if key not in invonhandset:
del invidsandnames[key]
missinginvnameswithids = invidsandnames
Have you considered making a temporary RDB (python has sqlite support baked in) and for reasonable numbers of items I don't think you would have a performance issues.
I would turn each CSV file and the result from the web-api into a tables (one table per data source). You can then do everything you want to do with some SQL queries + joins. Once you have the data you want, you can then dump it back to CSV.