To pickup from the comment: "I was doing this:"

df = [df.hc== 2]

What you create there is a "mask": an array with booleans that says which part of the index fulfilled your condition.

To filter your dataframe on your condition you want to do this:

df = df[df.hc == 2]

A bit more explicit is this:

mask = df.hc == 2
df = df[mask]

If you want to keep the entire dataframe and only want to replace specific values, there are methods such replace: Python pandas equivalent for replace. Also another (performance wise great) method would be creating a separate DataFrame with the from/to values as column and using pd.merge to combine it into the existing DataFrame. And using your index to set values is also possible:

df[mask]['fname'] = 'Johnson'

But for a larger set of replaces you would want to use one of the two other methods or use "apply" with a lambda function (for value transformations). Last but not least: you can use .fillna('bla') to rapidly fill up NA values.

In pandas the object type is used when there is not a clear distinction between the types stored in the column.

So, I guess that in your column, some objects are float type and some objects are str type. Or maybe, you are also dealing with NaN objects, NaN objects are float objects.

a) Convert the column to string: Are you getting your DataFrame from a CSV or XLS format file? Then at the moment of reading the file, you can specify that that column is an str type or just make the type conversion of the column you are dealing with.

b) After that, you can apply the string changes and/or deal with the NaN objects.

c) Finally, you transform your column into float type`.

Try this instead,

  print(
"{:.3f}% {} ({} sentences)".format(pcent, gender, nsents)
)

Refer the latest docs for more examples and check the Py version!

The error points to this line:

df['content'] = df['content'].apply(lambda x: " ".join(x.lower() for x in x.split() \
                                    if x not in stop_words))

split is being used here as a method of Python's built-in str class. Your error indicates one or more values in df['content'] is of type float. This could be because there is a null value, i.e. NaN, or a non-null float value.

One workaround, which will stringify floats, is to just apply str on x before using split:

df['content'] = df['content'].apply(lambda x: " ".join(x.lower() for x in str(x).split() \
                                    if x not in stop_words))

Alternatively, and possibly a better solution, be explicit and use a named function with a try / except clause:

def converter(x):
    try:
        return ' '.join([x.lower() for x in str(x).split() if x not in stop_words])
    except AttributeError:
        return None  # or some other value

df['content'] = df['content'].apply(converter)

Since pd.Series.apply is just a loop with overhead, you may find a list comprehension or map more efficient:

df['content'] = [converter(x) for x in df['content']]
df['content'] = list(map(converter, df['content']))

Take a look at the three parts of your code I've annotated with numbers in parentheses:

   def run(self,X,Y,lock):
        while True:
               #some calculations which returns x and y 
               with lock :
                 X.value = x
                 Y.value = y

                 # (3) you now try to access an attribute of the arguments
                 # called 'value'
                 print X.value , Y.value , self.process_id


if __name__ == '__main__':
    xL = Value('d',0.0) # (1) these variables are assigned some objects that
    xR = Value('d',0.0) # are returned by the function 'Value'
    yL = Value('d',0.0) 
    yR = Value('d',0.0) 

    lock = Lock()

    a = A('1')
    b = B('2')

    # (2) now your variables are being passed to the 'a.run' and 'b.run'
    # methods
    process_a = Process(target = a.run, args(xL,yL,lock, ))  
    process_b = Process(target = b.run, args(xR,yR,lock, ))

    process_a.start()
    process_b.start()

When you trace through the execution of your code, you can see that it is attempting to access an attribute called value in whatever objects are returned by the function Value. Your error message is telling you that Value is returning float objects, which do not have a value attribute.

One of the following things is (most likely) going wrong for you:

1. The function Value is not doing what you think it is, and it's returning floats when it shouldn't.
2. In your "some calculations which return x and y" code, you're overwriting the X and Y arguments with floats.

Skip the first df =:

for no_confi in range(len(df)): 
    if df['Confidence_Index_Status'][no_confi] == 0:
        df.iloc[no_confi,2:4] = np.nan
        df.iloc[no_confi,5:] = np.nan

An faster solution would be:

df.loc[df['Confidence_Index_Status'] == 0, 2:] = np.nan

The answer is already provided in the comments by @mattdmo and @tdelaney:

• NumPy 1.20 (release notes) deprecated numpy.float, numpy.int, and similar aliases, causing them to issue a deprecation warning

• NumPy 1.24 (release notes) removed these aliases altogether, causing an error when they are used

In many cases you can simply replace the deprecated NumPy types by the equivalent Python built-in type, e.g., numpy.float becomes a "plain" Python float.

For detailed guidelines on how to deal with various deprecated types, have a closer look at the table and guideline in the release notes for 1.20:

...

To give a clear guideline for the vast majority of cases, for the types bool, object, str (and unicode) using the plain version is shorter and clear, and generally a good replacement. For float and complex you can use float64 and complex128 if you wish to be more explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good and will not change behavior, but the precision will continue to depend on the computer and operating system. If you want to be more explicit and review the current use, you have the following alternatives:

• np.int64 or np.int32 to specify the precision exactly. This ensures that results cannot depend on the computer or operating system.
• np.int_ or int (the default), but be aware that it depends on the computer and operating system.
• The C types: np.cint (int), np.int_ (long), np.longlong.
• np.intp which is 32bit on 32bit machines 64bit on 64bit machines. This can be the best type to use for indexing.

...

If you have dependencies that use the deprecated types, a quick workaround would be to roll back your NumPy version to 1.24 or less (as suggested in some of the other answers), while waiting for the dependency to catch up. Alternatively, you could create a patch yourself and open a pull request, or monkey patch the dependency in your own code.

As pointed out by warren-weckesser this can also happen if you use dtype object (and in fact this is likelier the issue you are facing):

>>> s = pd.Series([1.0], dtype='object')
>>> s
0    1
dtype: object
>>> np.log(s)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'log'

You can address this by setting the dtype to float explicitly:

>>> np.log(s.astype('float64'))
0    0.0
dtype: float64

In your case:

np.log(df['price'].astype('float'))

Note: You can have more control using to_numeric.

First/alternative answer:

You have a float variable np in scope.

The problem is that:

import numpy as np
np = 1
np.log

is perfectly valid python.

>>> import numpy as np
>>> np = 1.
>>> np.log
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'log'

The solution is not to use np are a variable name, or other popular import abbreviations pd or dt etc. You can pick this kind of error up using a linter.

It can happen if there are NaN fields which can be solved by using dropna() :

pd.json_normalize(df.explode("field")["field"]))
=> AttributeError: 'float' object has no attribute 'items'

pd.json_normalize(df.explode("field")["field"].dropna())
=> no error

It's a just syntax error:

Put a colon (:) behind r instead of a point(.) and it will work.