np.loadtxt function showing error, could not convert string to float: '"Date"'

External question — Data Science Stack Exchange Author: coding_ninza Original post: https://datascience.stackexchange.com/questions/54818 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. This is my stock market csv data: Date,Open,High,Low,Close,Adj Close,Volume 43283,511,514.950012,503.5,512.599976,512.599976,261839 43284,512.599976,520,509.700012,512,512,332619 43285,512,515.950012,507.950012,514.299988,514.299988,173621 43286,515.549988,517.5,509.399994,510.899994,510.899994,117474 43287,510.049988,516.5,510.049988,514.25,514.25,82106 43290,514.200012,528.5,514.200012,523.650024,523.650024,322861 43291,530,534.900024,522.099976,532.549988,532.549988,404132 43292,533.400024,541.75,531,536.599976,536.599976,267510 43293,539.450012,545,535.25,537.25,537.25,254942 43294,540,540.799988,520.5,523.900024,523.900024,240378 43297,524,529.75,518.549988,523.099976,523.099976,191192 43298,523,540,519.799988,538.049988,538.049988,213308 43299,542.349976,542.799988,515.849976,524.200012,524.200012,557333 43300,528,536.900024,518.849976,527.299988,527.299988,201716 43301,527.599976,536.450012,524.950012,534.450012,534.450012,156703 43304,534.5,544.950012,531.049988,540.799988,540.799988,209083 43305,542.950012,549,538.450012,546,546,216217 43306,547,547.5,529.450012,531.849976,531.849976,145508 43307,537,543.900024,527,541.650024,541.650024,547093 43308,545,555,538,553.650024,553.650024,540695 43311,555,570,551.099976,568.450012,568.450012,564010 43312,582,584.950012,548,550.099976,550.099976,942588 43313,552.450012,555.549988,538.650024,544.900024,544.900024,440881 I am trying to load stock market data csv file in a jupyter note book using import numpy as np np.loadtxt(r"C:\Users\Souro\Downloads\Data.csv",delimiter=",") but it shows the following error after compiling: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-54-6552d575b229> in <module> ----> 1 np.loadtxt(r"C:\Users\Souro\Downloads\Data.csv",delimiter=",") c:\python3.7.2\lib\site-packages\numpy\lib\npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows) 1139 # converting the data 1140 X = None -> 1141 for x in read_data(_loadtxt_chunksize): 1142 if X is None: 1143 X = np.array(x, dtype) c:\python3.7.2\lib\site-packages\numpy\lib\npyio.py in read_data(chunk_size) 1066 1067 # Convert each value according to its column and store -> 1068 items = [conv(val) for (conv, val) in zip(converters, vals)] 1069 1070 # Then pack it according to the dtype's nesting c:\python3.7.2\lib\site-packages\numpy\lib\npyio.py in <listcomp>(.0) 1066 1067 # Convert each value according to its column and store -> 1068 items = [conv(val) for (conv, val) in zip(converters, vals)] 1069 1070 # Then pack it according to the dtype's nesting c:\python3.7.2\lib\site-packages\numpy\lib\npyio.py in floatconv(x) 773 if '0x' in x: 774 return float.fromhex(x) --> 775 return float(x) 776 777 typ = dtype.type ValueError: could not convert string to float: '"Date"' How can I get rid of this error?

External answer — Data Science Stack Exchange Author: Fatemeh Asgarinejad Original post: https://datascience.stackexchange.com/a/54819 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. The problem might arise because of the meta-text in the .csv or .txt file that is not really written there but is copied when its content is loaded somewhere. I think it is better to first import your text in an array or a string and then split it and save into the dataframe specifically when your data is not too large. import csv arrays = [] path = "C:\\Users\\Souro\\Downloads\\AXISBANK.csv" with open(path, 'r') as f: reader = csv.reader(f) for row in reader: row = str(row).replace('\\', '') # delete backslash arrays.append(row) Then take a look at arrays[:10] to find where the meta data ends and delete the unwanted data (meta data) and then converting the 'arrays' array into the dataframe. for instance: arrays = arrays[9:] df = pd.DataFrame(arrays[1:], columns=arrays[0]) #arrays[0] is the columns names About your comments: If you look at the text in each row (print each row), you would find out that a backslash is at the end of each row, so by row = str(row).replace('\\', '') we are substituting each backslash by nothing ( '' ), effectively deleting it. Why '\\' ? The backslash usually introduces an escape sequence (e.g. you can write '\n' for a newline character), so you have to escape it (raw parsing like r'a\b' works, but r'\' does not: the creator of Python chose for the latter to be considered a syntax error instead). And open('text.txt', 'r') opens the file text.txt in read-only mode ( r ).