Column y below should be ['Reg', 'Reg', 'Swp', 'Swp']
In [1]: pd.read_csv('/tmp/test3.csv')
Out[1]:
x,y
^@^@^@,Reg
^@^@^@,Reg
I,Swp
I,Swp
In [2]: ! cat /tmp/test3.csv
x y
0
1 NaN NaN
2 I Swp
3 I Swp
In [3]: f = open('/tmp/test3.csv', 'rb'); print(repr(f.read()))
'x,y\n \x00\x00\x00,Reg\n \x00\x00\x00,Reg\nI,Swp\nI,Swp\n'
Python | Pandas isnull() and notnull() While making a Data Frame from a csv file, many blank columns are imported as null value into the Data Frame which later creates problems while operating that data frame. Pandas isnull() and notnull() methods are used to check and manage NULL values in a data frame.
isnull. Detect missing values for an array-like object. This function takes a scalar or array-like object and indicates whether values are missing ( NaN in numeric arrays, None or NaN in object arrays, NaT in datetimelike).
The read_csv method, by default, reads all blank lines of an input CSV file.
If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x.
Yes, I could reproduce the problem, but don't know how to fix it with pd.read_csv
. Here is a workaround:
In [46]: import numpy as np
In [47]: arr = np.genfromtxt('test3.csv', delimiter = ',',
dtype = None, names = True)
In [48]: df = pd.DataFrame(arr)
In [49]: df
Out[49]:
x y
0 Reg
1 Reg
2 I Swp
3 I Swp
Note that with names = True
the first valid line of the csv is interpreted as column names (and therefore does not affect the dtype of the values on the subsequent lines.) Thus, if the csv file contains numerical data such as
In [22]: with open('/tmp/test.csv','r') as f:
....: print(repr(f.read()))
....:
'x,y,z\n \x00\x00\x00,Reg,1\n \x00\x00\x00,Reg,2\nI,Swp,3\nI,Swp,4\n'
Then genfromtxt will assign a numerical dtype to the third column (<i4
in this case).
In [19]: arr = np.genfromtxt('/tmp/test.csv', delimiter = ',', dtype = None, names = True)
In [20]: arr
Out[20]:
array([('', 'Reg', 1), ('', 'Reg', 2), ('I', 'Swp', 3), ('I', 'Swp', 4)],
dtype=[('x', '|S3'), ('y', '|S3'), ('z', '<i4')])
However, if the numerical data is intermingled with bytes such as '\x00'
then genfromtxt will be unable to recognize this column as numerical and will therefore resort to assigning a string dtype. Nevertheless, you can force the dtype of the columns by manually assigning the dtype
parameter. For example,
In [11]: arr = np.genfromtxt('/tmp/test.csv', delimiter = ',', dtype = [('x', '|i4'), ('y', '|S3')], names = True)
sets the first column x
to have dtype |i4
(4-byte integers) and the second column y
to have dtype |S3
(3-byte string). See this doc page for more information on available dtypes.
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