Menu
I need to iterate over each row of a pandas df and turn this into a comma separated string.
example:
df3 = DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e']) a b c d e 0 -0.158897 -0.749799 0.268921 0.070035 0.099600 1 -0.863654 -0.086814 -0.614562 -1.678850 0.980292 2 -0.098168 0.710652 -0.456274 -0.373153 -0.533463 3 1.001634 -0.736187 -0.812034 0.223062 -1.337972 4 0.173549 -0.576412 -1.016063 -0.217242 0.443794 5 0.273695 0.335562 0.778393 -0.668368 0.438880 6 -0.783824 1.439888 1.057639 -1.825481 -0.770953 7 -1.025004 0.155974 0.645023 0.993379 -0.812133 8 0.953448 -1.355628 -1.918317 -0.966472 -0.618744 9 -0.479297 0.295150 -0.294449 0.679416 -1.813078 I'd like to get for each row:
'-0.158897,-0.749799,0.268921,0.070035,0.099600' '0.863654,-0.086814,-0.614562,-1.678850,0.980292' ... and so on 
931
The command to convert Dataframe to list is pd. DataFrame. values. tolist().
Series and DataFrame methods define a . explode() method that explodes lists into separate rows. See the docs section on Exploding a list-like column. Since you have a list of comma separated strings, split the string on comma to get a list of elements, then call explode on that column.
To split cell into multiple rows in a Python Pandas dataframe, we can use the apply method. to call apply with a lambda function that calls str. split to split the x string value. And then we call explode to fill new rows with the split values.
How to Convert a Python List into a Comma-Separated String? You can use the . join string method to convert a list into a string. So again, the syntax is [seperator].
You could use pandas.DataFrame.to_string with some optional arguments set to False and then split on newline characters to get a list of your strings. This feels a little dirty though.
x = df3.to_string(header=False, index=False, index_names=False).split('\n') vals = [','.join(ele.split()) for ele in x] print(vals) Outputs:
['1.221365,0.923175,-1.286149,-0.153414,-0.005078', '-0.231824,-1.131186,0.853728,0.160349,1.000170', '-0.147145,0.310587,-0.388535,0.957730,-0.185315', '-1.658463,-1.114204,0.760424,-1.504126,0.206909', '-0.734571,0.908569,-0.698583,-0.692417,-0.768087', '0.000029,0.204140,-0.483123,-1.064851,-0.835931', '-0.108869,0.426260,0.107286,-1.184402,0.434607', '-0.692160,-0.376433,0.567188,-0.171867,-0.822502', '-0.564726,-1.084698,-1.065283,-2.335092,-0.083357', '-1.429049,0.790535,-0.547701,-0.684346,2.048081'] Use to_csv:
df = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e']) df.to_csv(header=None, index=False).strip('\n').split('\n') ['-1.60092768589,-0.746496859432,0.662527724304,-0.677984969682,1.70656657572', '-0.432306620615,-0.396499851892,0.564494290965,-1.01196068617,-0.630576490671', '-3.28916785414,0.627240166663,-0.359262938883,0.344156143177,-0.911269843378', '-0.272741450301,0.0594234886507,-2.72800253986,-0.821610087419,-0.0668212419497', '0.303490090149,-1.61344483051,0.117046351282,-1.46936429231,-0.66018613208', '-1.18157229705,-0.766519504863,0.386180129978,0.945274532852,-0.783459830884', '-1.27118723107,-1.12478330038,-0.625470220821,-0.453053132109,0.0641830786961', '-1.02657336234,-1.01556460318,0.445282883845,0.589873985417,-0.833648685855', '0.742343897524,-1.69644542886,-1.03886940911,0.511317569685,1.87084848086', '-0.159125435887,1.02522202275,0.254459603867,-0.487187861352,2.31900012693'] Note: this needs to be improved if you have \n in your cells.
26
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
