https://hashnode.com/utility/r?url=https%3A%2F%2Fcdn.hashnode.com%2Fres%2Fhashnode%2Fimage%2Fupload%2Fv1642053183825%2FTrLri6Qo6.jpeg%3Fw%3D1200%26h%3D630%26fit%3Dcrop%26crop%3Dentropy%26auto%3Dcompress%2Cformat%26format%3Dwebp%26fm%3Dpng

Laravel News Links

https://i.kinja-img.com/gawker-media/image/upload/c_fill,f_auto,fl_progressive,g_center,h_675,pg_1,q_80,w_1200/b5f236eb6fc1522972935ce44db5c088.gif

Gizmodo

https://static1.makeuseofimages.com/wordpress/wp-content/uploads/2022/01/shaking_hands_interview.jpg

MUO – Feed

https://www.youtube.com/embed/r9Gaauyf1Qk?feature=oembed

The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements.

This is Part 12 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame isna() & Dataframe isnull()

The DataFrame isna() and isnull() methods return Boolean (True/False) values in the same shape as the DataFrame/Series passed. If any empty values are of the following type, they will resolve to True.

• None
• NaN
• NaT
• NA

All other values (valid data) will resolve to False.

Note: Any empty strings or numpy.inf are not considered empty unless use_inf_as_na is set to True.

The syntax for these methods is as follows:

DataFrame.isna()
DataFrame.isnull()

Parameters:

These methods contain no parameters.

For this example, three (3) temperatures over three (3) days for Anchorage, Alaska save to a DataFrame. Unfortunately, some temperatures did not accurately record.

The code below returns a new DataFrame containing True values in the same position as the missing temperatures and False in the remainder.

Code – isna():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                         'Day-2':  [13, 14, pd.NaT],
                         'Day-3':  [None, 15, 16]},
                         index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.isna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses isna() to set the empty values (np.nan, pd.NaT, None) to True and the remainder (valid values) to False. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	True	False	True
Noon	False	False	False
Evening	False	True	False

Code – isnull():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.isnull()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses isnull() to set the empty values (np.nan, pd.NaT, None) to True and the remainder (valid values) to False. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	True	False	True
Noon	False	False	False
Evening	False	True	False

Note: The isnull() method is an alias of the isna() method. The output from both examples is identical.

DataFrame notna() & notnull()

The DataFrame notna() and notnull() methods return Boolean (True/False) values. These values returned are in the same shape as the DataFrame/Series passed. If any empty values are of the following type, they will resolve to False.

• None
• NaN
• NaT
• NA

All other values that are not of the above type (valid data) will resolve to True.

The syntax for these methods is as follows:

DataFrame.notna()
DataFrame.notnull()

Parameters:

These methods contain no parameters.

For this example, three (3) temperatures over three (3) days for Anchorage, Alaska save to a DataFrame. Unfortunately, some temperatures did not accurately record.

The code below returns a new DataFrame containing True values in the same position as the missing temperatures and False in the remainder.

Code – notna():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notna() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	False	True	False
Noon	True	True	True
Evening	True	False	True

Code – notnull():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notnull()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notnull() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	False	True	False
Noon	True	True	True
Evening	True	False	True

Note: The notnull() method is an alias of the notna() method. The output from both examples is identical.

DataFrame pad()

The pad() method is an alias for DataFrame/Series fillna() with the parameter method set to 'ffill'. Click here for details.

DataFrame replace()

The replace() method substitutes values in a DataFrame/Series with a different value assigned. This operation is performed dynamically on the object passed.

Note: The .loc/.iloc methods are slightly different from replace() as they require a specific location in order to change the said value(s).

The syntax for this method is as follows:

DataFrame.replace(to_replace=None, value=None, 
                  inplace=False, limit=None, 
                  regex=False, method='pad')

Parameter	Description
to_replace	Determines how to locate values to replace. The following parameters are: – Numeric, String, or Regex. – List of Strings, Regex, or Numeric. – Dictionary: a Dictionary, DataFrame Dictionary, or Nested Dictionary Each one must exactly match the to_replace parameter to cause any change.
value	The value to replace any values that match.
inplace	If set to True, the changes apply to the original DataFrame/Series. If False, the changes apply to a new DataFrame/Series. By default, False.
limit	The maximum number of elements to backward/forward fill.
regex	A regex expression to match. Matches resolve to the value parameter.
method	The available options for this method are pad, ffill, bfill, or None. Specify the replacement method to use.

Possible Errors Raised:

Error	When Does It Occur?
AssertionError	If regex is not a Boolean (True/False), or the to_replace parameter is None.
TypeError	If to_replace is not in a valid format, such as: – Not scalar, an array, a dictionary, or is None. – If to_replace is a dictionary and the value parameter is not a list. – If multiple Booleans or date objects and to_replace fails to match the value parameter.
ValueError	Any error returns if a list/ndarray and value are not the same length.

The examples below show how versatile the replace() method is. We recommend you spend some time reviewing the code and output.

In this example, we have five (5) grades for a student. Notice that one (1) grade is a failing grade. To rectify this, run the following code:

Code – Example 1

grades = pd.Series([55, 64, 52, 76, 49])
print(grades)

result = grades.replace(49, 51)
print(result)

• Line [1] creates a Series of Lists and saves it to grades.
• Line [2] modifies the failing grade of 49 to a passing grade of 51. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

O	55
1	64
2	52
3	76
4	51
dtype: int64

This example shows a DataFrame of three (3) product lines for Rivers Clothing. They want the price of 11.35 changed to 12.95. Run the code below to change the pricing.

Code – Example 2:

df = pd.DataFrame({'Tops':     [10.12, 12.23, 11.35],
                   'Tanks':    [11.35, 13.45, 14.98],
                   'Sweats':  [11.35, 21.85, 35.75]})

result = df.replace(11.35, 12.95)
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] replaces the value 11.35 to 12.95 for each occurrence. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

	Tops	Tanks	Sweats
0	10.12	12.95	12.95
1	12.23	13.45	21.85
2	12.95	14.98	35.75

Code – Example 3:

This example shows a DataFrame with two (2) teams. Each team contains three (3) members. This code removes one (1) member from each team and replaces it with quit.

df = pd.DataFrame({'Team-1': ['Barb', 'Todd', 'Taylor'],
                   'Team-2': ['Arch', 'Bart', 'Alex']})

result = df.replace(to_replace=r'^Bar.$', value='quit', regex=True)
print(result)

• Line [1] creates a Dictionary of Lists and saves it to df.
• Line [2] replaces any values that start with Bar and contain one (1) additional character (.). This match changed to the word quit. The output saves to result.
• Line [3] outputs the result to the terminal.

Finxter

https://www.futurity.org/wp/wp-content/uploads/2022/01/alzheimers-disease-neurodegeneration-1600.jpg

Boosting levels of the neurotransmitter norepinephrine with atomoxetine, a repurposed ADHD medication, may be able to stall neurodegeneration in people with early signs of Alzheimer’s disease, according to a new study.

The results appear in the journal Brain.

This is one of the first published clinical studies to show a significant effect on the protein tau, which forms neurofibrillary tangles in the brain in Alzheimer’s. In 39 people with mild cognitive impairment (MCI), six months of treatment with atomoxetine reduced levels of tau in study participants’ cerebrospinal fluid (CSF), and normalized other markers of neuro-inflammation.

The study points toward an alternative drug strategy against Alzheimer’s that does not rely on antibodies against tau or another Alzheimer’s-related protein, beta-amyloid. A recent FDA-approved drug, adacanumab, targets beta-amyloid but its benefits are controversial among experts in the field.

Larger and longer studies of atomoxetine in MCI and Alzheimer’s are warranted, the researchers conclude. The drug did not have a significant effect on cognition or other clinical outcomes, which was expected given the relatively short study duration.

“One of the major advantages of atomoxetine is that it is already FDA-approved and known to be safe,” says senior author David Weinshenker, professor of human genetics at Emory University School of Medicine. “The beneficial effects of atomoxetine on both brain network activity and CSF markers of inflammation warrant optimism.”

“We are encouraged by the results of the trial,” says lead author Allan Levey, professor of neurology at Emory University School of Medicine and director of the Goizueta Institute @Emory Brain Health. “The treatment was safe, well tolerated in individuals with mild cognitive impairment, and modulated the brain neurotransmitter norepinephrine just as we hypothesized. Moreover, our exploratory studies show promising results on imaging and spinal fluid biomarkers which need to be followed up in larger studies with longer period of treatment.”

The researchers picked atomoxetine, which is commercially available as Strattera, with the goal of boosting brain levels of norepinephrine, which they thought could stabilize a vulnerable region of the brain against Alzheimer’s-related neurodegeneration.

Norepinephrine is produced mainly by the locus coeruleus, a region of the brainstem that appears to be the first to show Alzheimer’s-related pathology—even in healthy, middle-aged people. Norepinephrine is thought to reduce inflammation and to encourage trash-removing cells called microglia to clear out aggregates of proteins such as beta-amyloid and tau. Increasing norepinephrine levels has positive effects on cognition and pathology in mouse and rat models of Alzheimer’s.

“Something that might seem obvious, but was absolutely essential, was our finding that atomoxetine profoundly increased CSF norepinephrine levels in these patients,” Weinshenker says. “For many drugs and trials, it is very difficult to prove target engagement. We were able to directly assess target engagement.”

Weinshenker also emphasizes that the trial grew out of pre-clinical research conducted in animal models, which demonstrated the potential for norepinephrine.

The researchers conducted the study between 2012 and 2018 with a cross-over design, such that half the group received atomoxetine for the first six months and the other half received placebo—then individuals switched. It is possible that participants who received atomoxetine for the first six months experienced carryover effects after treatment stopped, so their second six month period wasn’t necessarily a pure placebo.

Study participants were all diagnosed with mild cognitive impairment and had markers of potential progression to Alzheimer’s in their CSF, based on measuring tau and beta-amyloid. More information about inclusion criteria is available at clinicaltrials.gov.

The researchers measured levels of dozens of proteins in participants’ CSF; the reduction of tau from atomoxetine treatment was small—about 5% over six months—but if sustained, it could have a larger effect on Alzheimer’s pathology. No significant effect on beta-amyloid was seen.

In addition, in participants taking atomoxetine, researchers were able to detect an increase in metabolism in the medial temporal lobe, critical for memory, via PET (positron emission tomography) brain imaging.

Study participants started with a low dose of atomoxetine and ramped up to a higher dose, up to 100mg per day. Participants did experience weight loss (4 pounds, on average) and an increase in heart rate (about 5 beats per minute) while on atomoxetine, but they did not display a significant increase in blood pressure. Some people reported side effects such as gastrointestinal symptoms, dry mouth, or dizziness.

The FDA approved atomoxetine in 2002 for ADHD (attention deficit hyperactivity disorder) in children and adults, and the drug has been shown to be safe in older adults. It is considered to have low abuse potential, compared with conventional stimulants that are commonly prescribed for ADHD.

Looking ahead, it is now possible to visualize the integrity of the locus coeruleus in living people using MRI techniques, so that could be an important part of a larger follow-up study, Weinshenker says. Atomoxetine’s effects were recently studied in people with Parkinson’s disease—the benefits appear to be greater in those who have reduced integrity of the locus coeruleus.

Funding for the study was provided by the Cox and Kenan Family foundations and the Alzheimer’s Drug Discovery Foundation.

Source: Emory University

The post ADHD drug may protect against Alzheimer’s neurodegeneration appeared first on Futurity.

Futurity

I believe that armed self-defense is an extension of the Christian’s mandate to protect the innocent, to “watch over widows and orphans in their distress.” No one doubts that a policeman who carries a gun and watches over society can do such things and still be a Christian: Why, therefore, is there any doubt that an armed individual like me can carry a gun and watch over a small portion of society (my family) and yet still have a deep, abiding faith in God?

The Biblical Basis for Self-Defense – Ricochet

Go read the whole thing.

I like to say I am the most peaceful man you will ever know, but I am not a Pacifist.  Not understanding that difference can be hurtful to your health and ability to remain above room temperature. This does not counter the doctrine of the Catholic Church or any real Christian belief. It does piss off something fierce the Lefties using alleged Christian values and political jump points for the most antichrist behavior they can think of.

https://assets.amuniversal.com/c7a90d003b9f013a8b1f005056a9545d

Dilbert Daily Strip

https://media.notthebee.com/articles/61e1c1e66304661e1c1e663047.jpg

This may be the strangest display by a tenured academic ever caught on camera—and if you know higher ed, you know that’s a very high bar to clear:

Not the Bee

https://static1.makeuseofimages.com/wordpress/wp-content/uploads/2022/01/woman-programming-and-a-python-textbook.png

MUO – Feed

https://i.ytimg.com/vi/OdxVhb_kfvE/hqdefault.jpgIn this video, we will be looking at 6 new things that are added to Laravel version 8.78.0.Laravel News Links