Python convert int to uint

Convert int64 to uint64

Solution 1: Here are the lines where is constructed within : is a C-compatible (32bit) signed integer, and is a native Python integer, which may be either 32bit or 64bit depending on the platform. For example, on a 32bit system, where is 32bit: whereas on a 64bit system, where is 64bit: The check in the line above ensures that printing the is skipped for the appropriate platform-dependent native integer .

TypeError: data type ‘ int64’ not understood

I think you need specify dtypes in numpy :

For datetimes need different approach — parameter parse_dates in read_csv :

def load_dataset(x_path, y_path): x = pd.read_csv(os.sep.join([DATA_DIR, x_path]), dtype=DTYPES, index_col="ID" parse_dates='columnD') y = pd.read_csv(os.sep.join([DATA_DIR, y_path])) return x, y 

Convert int64 to object pandas Code Example, python dataframe convert text values to int64 pandas define column as interger turn pandas column to int read panda columns values as int set dataframe column to integer set the data as an integer in pandas pandas dataframe object to numeric transform column to int pandas try convert a column to int pandas turn data type …

Convert int64 to uint64

Use astype() to convert the values to another dtype:

import numpy as np (a+2**63).astype(np.uint64) # array([ 0, 18446744073709551615], dtype=uint64) 

I’m not a real numpy expert, but this:

>>> a = np.array([-2**63,2**63-1], dtype=np.int64) >>> b = np.array([x+2**63 for x in a], dtype=np.uint64) >>> b array([ 0, 18446744073709551615], dtype=uint64) 

works for me with Python 2.6 and numpy 1.3.0

I assume you meant 2**64-1 , not 2**64 , in your expected output, since 2**64 won’t fit in a uint64. (18446744073709551615 is 2**64-1 )

How to convert object data type into int64 in python?, You can try by doing df [«Bare Nuclei»].astype (np.int64) but as far as I can see the problem is something else. Pandas first reads all the data to best estimate the data type for each column, then only makes the data frame. So, there must be some entries in the data frame which are not integer types, i.e., they may …

Python: TypeError: ‘numpy.int64’ object is not iterable

I have previously encountered the same issue. This bug has to deal with your for statements. You might want to try changing your code to:

for column in range(unique_elements): #add translation to the dictionary 

Python 3.x — How can I initialize and use 64-bit integer in, Since Python’s int is essentially boundless there will never be an overflow issue. Frankly, sometimes it is bad to use unlimited integers in Python. Best alternative is to use NumPy fixed length types if you really need exactly 32bit or 64bit ops. import numpy as np v = np.uint64 (99) q = np.uint64 (12) * v + np.uint64 (77) print (q) print Code samplev = np.uint64(99)q = np.uint64(12) * v + np.uint64(77)print(q)print(type(q))Feedback

Why are there two np.int64s in numpy.core.numeric._typelessdata (Why is numpy.int64 not numpy.int64?)

Here are the lines where _typelessdata is constructed within numeric.py :

_typelessdata = [int_, float_, complex_] if issubclass(intc, int): _typelessdata.append(intc) if issubclass(longlong, int): _typelessdata.append(longlong) 

intc is a C-compatible (32bit) signed integer, and int is a native Python integer, which may be either 32bit or 64bit depending on the platform.

On a 32bit system the native Python int type is also 32bit, so issubclass(intc, int) returns True and intc gets appended to _typelessdata , which ends up looking like this:

[numpy.int32, numpy.float64, numpy.complex128, numpy.int32] 

[numpy.int64, numpy.float64, numpy.complex128, numpy.int64] 

The bigger question is why the contents of _typelessdata are set like this. The only place I could find in the numpy source where _typelessdata is actually used is this line within the definition for np.array_repr in the same file:

skipdtype = (arr.dtype.type in _typelessdata) and arr.size > 0 

The purpose of _typelessdata is to ensure that np.array_repr correctly prints the string representation of arrays whose dtype happens to be the same as the (platform-dependent) native Python integer type.

For example, on a 32bit system, where int is 32bit:

In [1]: np.array_repr(np.intc([1])) Out[1]: 'array([1])' In [2]: np.array_repr(np.longlong([1])) Out[2]: 'array([1], dtype=int64)' 

whereas on a 64bit system, where int is 64bit:

In [1]: np.array_repr(np.intc([1])) Out[1]: 'array([1], dtype=int32)' In [2]: np.array_repr(np.longlong([1])) Out[2]: 'array([1])' 

The arr.dtype.type in _typelessdata check in the line above ensures that printing the dtype is skipped for the appropriate platform-dependent native integer dtypes .

I don’t know the full history behind it, but the second int64 is actually numpy.longlong .

In [1]: import numpy as np In [2]: from numpy.core.numeric import _typelessdata In [3]: _typelessdata Out[4]: [numpy.int64, numpy.float64, numpy.complex128, numpy.int64] In [5]: id(_typelessdata[-1]) == id(np.longlong) Out[5]: True 

numpy.longlong is supposed to directly correspond to C’s long long type. C’s long long is specified to be at least 64 bits wide, but the exact definition is left up to the compiler.

My guess is that numpy.longlong winds up being another instance of numpy.int64 on most systems, but is allowed to be something different if the C complier defines long long as something wider than 64 bits.

Python Pandas — Series, #import the pandas library and aliasing as pd import pandas as pd import numpy as np s = pd.Series(5, index= [0, 1, 2, 3]) print s Its output is as follows − 0 5 1 5 2 5 3 5 dtype: int64 Accessing Data from Series with Position Data in the series can be accessed similar to that in an ndarray. Example 1 Retrieve the first element.

Источник

Convert int64 to uint64

Solution 1: Here are the lines where is constructed within : is a C-compatible (32bit) signed integer, and is a native Python integer, which may be either 32bit or 64bit depending on the platform. For example, on a 32bit system, where is 32bit: whereas on a 64bit system, where is 64bit: The check in the line above ensures that printing the is skipped for the appropriate platform-dependent native integer .

TypeError: data type ‘ int64’ not understood

I think you need specify dtypes in numpy :

For datetimes need different approach — parameter parse_dates in read_csv :

def load_dataset(x_path, y_path): x = pd.read_csv(os.sep.join([DATA_DIR, x_path]), dtype=DTYPES, index_col="ID" parse_dates='columnD') y = pd.read_csv(os.sep.join([DATA_DIR, y_path])) return x, y 

Convert int64 to object pandas Code Example, python dataframe convert text values to int64 pandas define column as interger turn pandas column to int read panda columns values as int set dataframe column to integer set the data as an integer in pandas pandas dataframe object to numeric transform column to int pandas try convert a column to int pandas turn data type …

Convert int64 to uint64

Use astype() to convert the values to another dtype:

import numpy as np (a+2**63).astype(np.uint64) # array([ 0, 18446744073709551615], dtype=uint64) 

I’m not a real numpy expert, but this:

>>> a = np.array([-2**63,2**63-1], dtype=np.int64) >>> b = np.array([x+2**63 for x in a], dtype=np.uint64) >>> b array([ 0, 18446744073709551615], dtype=uint64) 

works for me with Python 2.6 and numpy 1.3.0

I assume you meant 2**64-1 , not 2**64 , in your expected output, since 2**64 won’t fit in a uint64. (18446744073709551615 is 2**64-1 )

How to convert object data type into int64 in python?, You can try by doing df [«Bare Nuclei»].astype (np.int64) but as far as I can see the problem is something else. Pandas first reads all the data to best estimate the data type for each column, then only makes the data frame. So, there must be some entries in the data frame which are not integer types, i.e., they may …

Python: TypeError: ‘numpy.int64’ object is not iterable

I have previously encountered the same issue. This bug has to deal with your for statements. You might want to try changing your code to:

for column in range(unique_elements): #add translation to the dictionary 

Python 3.x — How can I initialize and use 64-bit integer in, Since Python’s int is essentially boundless there will never be an overflow issue. Frankly, sometimes it is bad to use unlimited integers in Python. Best alternative is to use NumPy fixed length types if you really need exactly 32bit or 64bit ops. import numpy as np v = np.uint64 (99) q = np.uint64 (12) * v + np.uint64 (77) print (q) print Code samplev = np.uint64(99)q = np.uint64(12) * v + np.uint64(77)print(q)print(type(q))Feedback

Why are there two np.int64s in numpy.core.numeric._typelessdata (Why is numpy.int64 not numpy.int64?)

Here are the lines where _typelessdata is constructed within numeric.py :

_typelessdata = [int_, float_, complex_] if issubclass(intc, int): _typelessdata.append(intc) if issubclass(longlong, int): _typelessdata.append(longlong) 

intc is a C-compatible (32bit) signed integer, and int is a native Python integer, which may be either 32bit or 64bit depending on the platform.

On a 32bit system the native Python int type is also 32bit, so issubclass(intc, int) returns True and intc gets appended to _typelessdata , which ends up looking like this:

[numpy.int32, numpy.float64, numpy.complex128, numpy.int32] 

[numpy.int64, numpy.float64, numpy.complex128, numpy.int64] 

The bigger question is why the contents of _typelessdata are set like this. The only place I could find in the numpy source where _typelessdata is actually used is this line within the definition for np.array_repr in the same file:

skipdtype = (arr.dtype.type in _typelessdata) and arr.size > 0 

The purpose of _typelessdata is to ensure that np.array_repr correctly prints the string representation of arrays whose dtype happens to be the same as the (platform-dependent) native Python integer type.

For example, on a 32bit system, where int is 32bit:

In [1]: np.array_repr(np.intc([1])) Out[1]: 'array([1])' In [2]: np.array_repr(np.longlong([1])) Out[2]: 'array([1], dtype=int64)' 

whereas on a 64bit system, where int is 64bit:

In [1]: np.array_repr(np.intc([1])) Out[1]: 'array([1], dtype=int32)' In [2]: np.array_repr(np.longlong([1])) Out[2]: 'array([1])' 

The arr.dtype.type in _typelessdata check in the line above ensures that printing the dtype is skipped for the appropriate platform-dependent native integer dtypes .

I don’t know the full history behind it, but the second int64 is actually numpy.longlong .

In [1]: import numpy as np In [2]: from numpy.core.numeric import _typelessdata In [3]: _typelessdata Out[4]: [numpy.int64, numpy.float64, numpy.complex128, numpy.int64] In [5]: id(_typelessdata[-1]) == id(np.longlong) Out[5]: True 

numpy.longlong is supposed to directly correspond to C’s long long type. C’s long long is specified to be at least 64 bits wide, but the exact definition is left up to the compiler.

My guess is that numpy.longlong winds up being another instance of numpy.int64 on most systems, but is allowed to be something different if the C complier defines long long as something wider than 64 bits.

Python Pandas — Series, #import the pandas library and aliasing as pd import pandas as pd import numpy as np s = pd.Series(5, index= [0, 1, 2, 3]) print s Its output is as follows − 0 5 1 5 2 5 3 5 dtype: int64 Accessing Data from Series with Position Data in the series can be accessed similar to that in an ndarray. Example 1 Retrieve the first element.

Источник