Use the ‘csv’ library. First, register your dialect: import csv csv.register_dialect(‘piper’, delimiter=”|”, quoting=csv.QUOTE_NONE) Then, use your dialect on the file: with open(myfile, “rb”) as csvfile: for row in csv.DictReader(csvfile, dialect=”piper”): print row[‘name’]
In this SO post, there are two answers that -combined together- provide a nice solution for successfully using ON CONFLICT. The example below, uses ON CONFLICT DO NOTHING;: BEGIN; CREATE TEMP TABLE tmp_table (LIKE main_table INCLUDING DEFAULTS) ON COMMIT DROP; COPY tmp_table FROM ‘full/file/name/here’; INSERT INTO main_table SELECT * FROM tmp_table ON CONFLICT DO NOTHING; … Read more
This looks like a job for DictWriter: import csv import itertools import sys fields = [ ‘org’, ‘2015’, ‘2014’, ‘2013’ ] dw = { ‘orgname1’: { ‘2015’ : 2, ‘2014’ : 1, ‘2013’ : 1 }, ‘orgname2’: { ‘2015’ : 1, ‘2014’ : 2, ‘2013’ : 3 }, ‘orgname3’: { ‘2015’ : 1, ‘2014’ : … Read more
In order to bucket your series, you should use the pd.cut() function, like this: df[‘bin’] = pd.cut(df[‘1’], [0, 50, 100,200]) 0 1 file bin 0 person1 24 age.csv (0, 50] 1 person2 17 age.csv (0, 50] 2 person3 98 age.csv (50, 100] 3 person4 6 age.csv (0, 50] 4 person2 166 Height.csv (100, 200] 5 … Read more
Use d3.text to load the data, and then d3.csvParseRows to parse it. For example: d3.text(“data/testnh.csv”, function(text) { console.log(d3.csvParseRows(text)); }); You’ll probably also want to convert your columns to numbers, because they’ll be strings by default. Assume they are all numbers, you could say: d3.text(“data/testnh.csv”, function(text) { var data = d3.csvParseRows(text).map(function(row) { return row.map(function(value) { return … Read more
Use the csv module: import csv … with open(in_fnam, newline=””) as in_file: with open(out_fnam, ‘w’, newline=””) as out_file: writer = csv.writer(out_file) for row in csv.reader(in_file): if row: writer.writerow(row) If you also need to remove rows where all of the fields are empty, change the if row: line to: if any(row): And if you also want … Read more
You can split the string manually: >>> df[‘Tags’] = df.Tags.apply(lambda x: x[1:-1].split(‘,’)) >>> df.Tags[0] [‘Tag1’, ‘Tag2’]
Documentation says that you should use open(‘eggs.csv’, ‘w’, newline=””) http://docs.python.org/py3k/library/csv.html#id2
You can actually do this and have Excel show a clickable link. Use this format in the CSV file: =HYPERLINK(“URL”) So the CSV would look like: 1,23.4,=HYPERLINK(“http://www.google.com”) However, I’m trying to get some links with commas in them to work properly and it doesn’t look like there’s a way to escape them and still have … Read more