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`.

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

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.

df = df.drop(index, inplace = True) Using inplace = True changes the return value of df.drop() to None. Either don't use inplace df = df.drop(index) Or don't try to assign the return value back to the dataframe df.drop(index, inplace = True)

You don't need a double assignment. Try this:

data.loc[data['Value2'] < 10, 'Value1'] = np.nan

Remember pd.DataFrame.loc can be used both as a "getter" and as a "setter". So it is perfectly natural to assign a value to data.loc[slice1, slice2].

Hi @Jalvear, The input tables that are available in the “Python Script (Labs)” node are not pandas DataFrames. Therefore, you can’t call loc on them. This is done for performance reasons because converting data to pandas is slow and we only want to do it if necessary. Just call to_pandas on the input table to convert it to a pandas DataFrame (e.g. input_table_df = knio.input_tables[0].to_pandas()). We have some examples of how to use the API of the “Python Script (Labs)” node here: KNIME Python Integration Guide. See the example called “Converting input tables to Pandas DataFrames and PyArrow Tables”. Also, you can find API references here: Contents — KNIME Python Script (Labs) API documentation.

Hey there @core6539010994 :grinning: @toastedpitabread is correct that .isalpha() is a string method, hence your error. There are a few different ways you could do this, though personaly I would use a try/except statement, which is practicly a way of rerouting your code if an error is thrown. F…

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']))

Try this instead,

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

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

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.

Seems like sympy.log doesn't return a plain number but something of type log or sympy.core.numbers.Float. Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) [GCC 7.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import sympy >>> sympy.__version__ '1.1.1' >>> sympy.log(123), sympy.log(123.0) (log(123), 4.81218435537242) >>> type(sympy.log(123)), type(sympy.log(123.0)) (log, ) >>> sympy.log(123).evalf() 4.81218435537242 You can use float(sympy.log(x)) to get a number. Since this is the same type as math.log it should solve your problem. >>> type(float(sy.log(123))) == type(math.log(123)) True >>> type(float(sy.log(123.0))) == type(math.log(123.0)) True To me it seems like maybe you'd want to use scipy.log or numpy.log directly.

Probably there's something wrong with the input values for X and/or T. The function from the question works ok:

import numpy as np
from math import e

def sigmoid(X, T):
  return 1.0 / (1.0 + np.exp(-1.0 * np.dot(X, T)))

X = np.array([[1, 2, 3], [5, 0, 0]])
T = np.array([[1, 2], [1, 1], [4, 4]])

print(X.dot(T))
# Just to see if values are ok
print([1. / (1. + e ** el) for el in [-5, -10, -15, -16]])
print()
print(sigmoid(X, T))

Result:

[[15 16]
 [ 5 10]]

[0.9933071490757153, 0.9999546021312976, 0.999999694097773, 0.9999998874648379]

[[ 0.99999969  0.99999989]
 [ 0.99330715  0.9999546 ]]

Probably it's the dtype of your input arrays. Changing X to:

X = np.array([[1, 2, 3], [5, 0, 0]], dtype=object)

Gives:

Traceback (most recent call last):
  File "/[...]/stackoverflow_sigmoid.py", line 24, in <module>
    print sigmoid(X, T)
  File "/[...]/stackoverflow_sigmoid.py", line 14, in sigmoid
    return 1.0 / (1.0 + np.exp(-1.0 * np.dot(X, T)))
AttributeError: exp

cal_time = 0.0 # the time to dequeue all the elements Why would the number 0.0 have an attribute "time"?