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Is there a job scheduler library for node.js? [closed]
... answered Jun 12 '14 at 4:43
XåpplI'-I0llwlg'I -XåpplI'-I0llwlg'I -
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How do you do a case insensitive search using a pattern modifier using less?
...ed Aug 19 '08 at 20:09
Juha SyrjäläJuha Syrjälä
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What is the $$hashKey added to my JSON.stringify result
...'14 at 5:48
Michael Falck WedelgårdMichael Falck Wedelgård
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How to use OrderBy with findAll in Spring Data
... redundant public modifier is also a good idea ;)
– Håvard Geithus
Apr 20 '15 at 17:11
1
I agree...
How to export DataTable to Excel
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Try simple code, to convert DataTable to excel file as csv:
var lines = new List<string>();
string[] columnNames = dataTable.Columns
.Cast<DataColumn>()
.Select(column => column.ColumnName)
.ToArray();
var header = string.Join(",", columnNames.Select...
Eclipse Kepler for OS X Mavericks request Java SE 6
... answered Nov 3 '13 at 0:08
Kåre JonssonKåre Jonsson
3122 bronze badges
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Subset of rows containing NA (missing) values in a chosen column of a data frame
We have a data frame from a CSV file. The data frame DF has columns that contain observed values and a column ( VaR2 ) that contains the date at which a measurement has been taken. If the date was not recorded, the CSV file contains the value NA , for missing data.
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Split a string by spaces — preserving quoted substrings — in Python
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Depending on your use case, you may also want to check out the csv module:
import csv
lines = ['this is "a string"', 'and more "stuff"']
for row in csv.reader(lines, delimiter=" "):
print(row)
Output:
['this', 'is', 'a string']
['and', 'more', 'stuff']
...
List of tables, db schema, dump etc using the Python sqlite3 API
...se.db')
table = pd.read_sql_query("SELECT * from table_name", db)
table.to_csv(table_name + '.csv', index_label='index')
Dump all tables:
import sqlite3
import pandas as pd
def to_csv():
db = sqlite3.connect('database.db')
cursor = db.cursor()
cursor.execute("SELECT name FROM sqlite...
How do I convert dates in a Pandas data frame to a 'date' data type?
...
I imagine a lot of data comes into Pandas from CSV files, in which case you can simply convert the date during the initial CSV read:
dfcsv = pd.read_csv('xyz.csv', parse_dates=[0]) where the 0 refers to the column the date is in.
You could also add , index_col=0 in there...