I think you can use dask.dataframe.from_pandas: from dask import dataframe as dd sd = dd.from_pandas(df, npartitions=3) print (sd) dd.DataFrame<from_pa…, npartitions=2, divisions=(0, 1, 2)> EDIT: I find solution: import pandas as pd import dask.dataframe as dd from dask.dataframe.utils import make_meta df=pd.DataFrame({‘a’:[1,2,3],’b’:[4,5,6]}) dsk = {(‘x’, 0): df} meta = make_meta({‘a’: ‘i8’, ‘b’: ‘i8’}, index=pd.Index([], ‘i8’)) d = … Read more
Update 2019: There is no need of using any external tool or Heck as pointed by older answers. Android Studio’s Asset Studio allows to convert SVG/ PSD to vectors Right click on app folder-> New Vector Asset Select second option in radio button to create vector from local file as shown in below image. Click … Read more
-(NSString*) suffixNumber:(NSNumber*)number { if (!number) return @””; long long num = [number longLongValue]; int s = ( (num < 0) ? -1 : (num > 0) ? 1 : 0 ); NSString* sign = (s == -1 ? @”-” : @”” ); num = llabs(num); if (num < 1000) return [NSString stringWithFormat:@”%@%lld”,sign,num]; int exp = … Read more
Do a replace first: parseFloat(str.replace(‘,’,’.’).replace(‘ ‘,”))
ByteBuffer has this capability, and is able to work with both little and big endian integers. Consider this example: // read the file into a byte array File file = new File(“file.bin”); FileInputStream fis = new FileInputStream(file); byte [] arr = new byte[(int)file.length()]; fis.read(arr); // create a byte buffer and wrap the array ByteBuffer bb … Read more
How about this? “1,112”.delete(‘,’).to_i
New solution: Built using Your great comments, thanks! SELECT JSON_ARRAYAGG(JSON_OBJECT(‘name’, name, ‘phone’, phone)) from Person; Old solution: With help from @Schwern I managed to put up this query, which seems to work! SELECT CONCAT( ‘[‘, GROUP_CONCAT(JSON_OBJECT(‘name’, name, ‘phone’, phone)), ‘]’ ) FROM person;
Maybe this might help: JSefa You can read CSV file with this tool and serialize it to XML.
int(True) is 1. 1 is: 00000001 and ~1 is: 11111110 Which is -2 in Two’s complement1 1 Flip all the bits, add 1 to the resulting number and interpret the result as a binary representation of the magnitude and add a negative sign (since the number begins with 1): 11111110 → 00000001 → 00000010 ↑ … Read more
import csv to_csv = [ {‘name’: ‘bob’, ‘age’: 25, ‘weight’: 200}, {‘name’: ‘jim’, ‘age’: 31, ‘weight’: 180}, ] keys = to_csv[0].keys() with open(‘people.csv’, ‘w’, newline=””) as output_file: dict_writer = csv.DictWriter(output_file, keys) dict_writer.writeheader() dict_writer.writerows(to_csv)