While the answer here is as requested within tensorboard it only allows to download a csv for a single run of a single tag. If you have for example 10 tags and 20 runs (what is not at all much) you would need to do the above step 200 times (that alone will probably take … Read more
It may be easier to use mounting instead of pydrive. from google.colab import drive drive.mount(‘drive’) After authentication, you can copy your csv file. df.to_csv(‘data.csv’) !cp data.csv “drive/My Drive/”
In version 0.19.0 you can use parameter dtype=”category” in read_csv: data=”col1,col2,col3\na,b,1\na,b,2\nc,d,3″ df = pd.read_csv(pd.compat.StringIO(data), dtype=”category”) print (df) col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 print (df.dtypes) col1 category col2 category col3 category dtype: object If want specify column for category use dtype with dictionary: df = pd.read_csv(pd.compat.StringIO(data), … Read more
Not much… One is that, these bugs show up rarely with test data, but not so rarely in real world. All it takes is one case. Test data is not random data, one user with one failure case should submit the data and voilà, we’ve got a test case. If no one can provide test … Read more
For tab-delimited files, http://www.rfc-editor.org/rfc/rfc4180.txt mentions ‘text/tab-separated-values’. That format is (sparsely) described at http://www.iana.org/assignments/media-types/text/tab-separated-values. More info can be found at : http://www.cs.tut.fi/~jkorpela/TSV.html I hope this helps?
Step 1: Don’t do this: while(!file.eof()) { getline(file,line); numlines++; } numline–; The EOF is not true until you try and read past it. The standard pattern is: while(getline(file,line)) { ++numline; } Also note that std::getline() can optionally take a third parameter. This is the character to break on. By default this is the line terminator … Read more
You are very close; currently you compare the row[2] with integer 0, make the comparison with the string “0”. When you read the data from a file, it is a string and not an integer, so that is why your integer check fails currently: row[2]!=”0″: Also, you can use the with keyword to make the … Read more
Use append=True in the call to to_hdf: import numpy as np import pandas as pd filename=”/tmp/test.h5″ df = pd.DataFrame(np.arange(10).reshape((5,2)), columns=[‘A’, ‘B’]) print(df) # A B # 0 0 1 # 1 2 3 # 2 4 5 # 3 6 7 # 4 8 9 # Save to HDF5 df.to_hdf(filename, ‘data’, mode=”w”, format=”table”) del df … Read more
You can still use your line, if you declare the headers yourself, since you know it: with open(‘data.csv’) as f: cf = csv.DictReader(f, fieldnames=[‘city’]) for row in cf: print row[‘city’] For more information check csv.DictReader info in the docs. Another option is to just use positional indexing, since you know there’s only one column: with … Read more