Nuffing.com

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What… in… the… world?

Not the Bee

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sudo apt install cmatrix

sudo pacman -S cmatrix

sudo dnf install cmatrix

cowsay "Mooooo"

sudo apt install cowsay

sudo pacman -S cowsay

sudo dnf install cowsay

sl

sudo apt install sl

sudo pacman -S sl

sudo dnf install sl

figlet "Your string here"

sudo apt install figlet

sudo pacman -S figlet

sudo dnf install figlet

fortune

cowsay | fortune

sudo apt install fortune

sudo pacman -S fortune-mod

sudo dnf install fortune-mod

sudo apt install x11-apps

sudo pacman -S xorg-xeyes

sudo dnf install xeyes

sudo apt install libaa-bin

sudo pacman -S aalib

sudo dnf install aalib

espeak "Hello World"

sudo apt install espeak

yay -S espeak

sudo dnf install espeak

sudo add-apt-repository ppa:ytvwld/asciiquarium
sudo apt install asciiquarium

sudo pacman -S asciiquarium

sudo dnf install asciiquarium

sudo apt install rig

yay -S rig

sudo dnf install rig

MUO – Feed

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Next Post Coming Soon…▶

Holosun just introduced their new RML Rail Mounted Laser. It’s tiny and affordable and will come in both red and green laser versions. The RML will come in five models with MSRPs ranging from $105 to $162 and is expected to hit stores in March or April.

Here’s their press release . . .

Lasers are becoming invaluable to verify an accurate and effective aim, especially in low-light environments. Pistols and rifles fixed with lasers have been shown to improve fast target acquisition. With the growth of red dot optics, lasers have fast been growing in the industry as an alternative to mounted optics. Not only does this help to improve users’ response time, but it also makes a potential Point of Impact clear.

Holosun is known for optics and lasers. This year, Holosun releases the RML (Rail-Mounted Laser). The RML comes in at a very manageable 1.97″×1.18″×0.91″ and 1.3 ounces. Made with a durable polymer housing, the RML is IPX8 rated for water and dust resistance. Additionally, Holosun tests each unit to 2,000G shock resistance. This guarantees that the RML is suited for use in extreme environments.

The RML is available in either a red or green laser version, both of which are class 3R and <5mW output power. The RML package includes one CR1/3N lithium battery. The laser can be adjusted by 4MOA per click and can travel a total of +/-60 MOA. The rate of travel makes it ideal for a primary or even secondary zero, providing an alternate distance point of aim from iron sights or a pistol mounted optic.

With many features, it is easy to see why the RML is a strong contender. Holosun has made it easy to utilize the laser in multiple roles with multiple color options. For the hiker who carries a defensive pistol, the uniformed officer that relies on an alternate color laser and red dot, and everything in between, the RML fills their needs.

Specifications:

• 520nm Green or 635nm Red, class 3R laser
• Cr1/3N removable battery
• Durable Polymer housing
• 4 MOA adjustment per click
• +/- 60 MOA laser W&E travel range
• IPX8 water & dust resistance
• 2000G vibration resistance
• Dimensions: 1.97×1.18×0.91
• Weight: 1.8oz

 

Next Post Coming Soon…▶

The Truth About Guns

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Are you ready to head back to Middle Earth? Amazon Studios revealed the title and trailer Wednesday for its highly anticipated prequel to the “Lord of the Rings” series, called “The Lord of the Rings: The Rings of Power.”

The series will debut on Prime Video on Sept. 2.

“The Rings of Power” is set in the Second Age of Middle Earth, thousands of years before the events of J.R.R. Tolkien’s “The Hobbit” and “The Lord of the Rings.”

The series “will take viewers back to an era in which great powers were forged, kingdoms rose to glory and fell to ruin, unlikely heroes were tested, hope hung by the finest of threads, and the greatest villain that ever flowed from Tolkien’s pen threatened to cover all the world in darkness,” Amazon said in its YouTube description for the trailer.

Amazon founder Jeff Bezos tweeted an image of himself holding a big slab of wood with the series title on it. “Can’t wait for you to see it,” he wrote.

IGN has behind-the-scenes details on how the title sequence was created, and it wasn’t with CGI, but rather with molten metal and a “hunk of reclaimed redwood.”

Amazon first announced that it had acquired the rights to adapt Tolkien’s work in 2017.

“’The Lord of the Rings’ is a cultural phenomenon that has captured the imagination of generations of fans through literature and the big screen,” Sharon Tal Yguado, head of Scripted Series for Amazon Studios, said in a statement at the time. 

Tolkien’s book series was named Amazon customers’ favorite book of the millennium in 1999. Director Peter Jackson’s theatrical adaptations included “The Fellowship of the Ring” (2001); “The Two Towers” (2002); and “The Return of the King” (2003). The films grossed nearly $6 billion worldwide and won a combined 17 Academy Awards, including Best Picture for “King.”

GeekWire

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Link

Rhys Darby (Murray from Flight of the Conchords) stars in this high-seas comedy adventure series about a wealthy man who abandons his life of privilege to become a pirate. Taika Waititi, the busiest man in Hollywood, does double duty as Executive Producer and performs as Blackbeard. Premieres 3.2022 on HBO Max.

The Awesomer

http://img.youtube.com/vi/PMKuZoQoYE0/0.jpg

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 13 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()
• Part 13 focuses on the DataFrame methods drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values() and sort_index()

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 drop_level()

The drop_level() method removes the specified index or column from a DataFrame/Series. This method returns a DataFrame/Series with the said level/column removed.

The syntax for this method is as follows:

DataFrame.droplevel(level, axis=0)

For this example, we generate random stock prices and then drop (remove) level Stock-B from the DataFrame.

nums = np.random.uniform(low=0.5, high=13.3, size=(3,4))
df_stocks = pd.DataFrame(nums).set_index([0, 1]).rename_axis(['Stock-A', 'Stock-B'])
print(df_stocks)

result = df_stocks.droplevel('Stock-B')
print(result)

• Line [1] generates random numbers for three (3) lists within the specified range. Each list contains four (4) elements (size=3,4). The output saves to nums.
• Line [2] creates a DataFrame, sets the index, and renames the axis. This output saves to df_stocks.
• Line [3] outputs the DataFrame to the terminal.
• Line [4] drops (removes) Stock-B from the DataFrame and saves it to the result variable.
• Line [5] outputs the result to the terminal.

Output:

df_stocks

result

DataFrame pivot()

The pivot() method reshapes a DataFrame/Series and produces/returns a pivot table based on column values.

The syntax for this method is as follows:

DataFrame.pivot(index=None, columns=None, values=None)

For this example, we generate 3-day sample stock prices for Rivers Clothing. The column headings display the following characters.

• A (for Opening Price)
• B (for Midday Price)
• C (for Opening Price)

cdate_idx = ['01/15/2022', '01/16/2022', '01/17/2022'] * 3
group_lst = list('AAABBBCCC')
vals_lst  = np.random.uniform(low=0.5, high=13.3, size=(9))

df = pd.DataFrame({'dates':  cdate_idx,
                                    'group':  group_lst,
                                   'value':  vals_lst})
print(df)

result = df.pivot(index='dates', columns='group', values='value')
print(result)

• Line [1] creates a list of dates and multiplies this by three (3). The output is three (3) entries for each date. This output saves to cdate_idx.
• Line [2] creates a list of headings for the columns (see above for definitions). Three (3) of each character are required (9 characters). This output saves to group_lst.
• Line [3] uses np.random.uniform to create a random list of nine (9) numbers between the set range. The output saves to vals_lst.
• Line [4] creates a DataFrame using all the variables created on lines [1-3]. The output saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] creates a pivot from the DataFrame and groups the data by dates. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

result

DataFrame pivot_table()

The pivot_table() method streamlines a DataFrame to contain only specific data (columns). For example, say we have a list of countries with associated details. We only want to display one or two columns. This method can accomplish this task.

The syntax for this method is as follows:

DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters.

Code – Example 1:

df = pd.read_csv('countries.csv')
df = df.head(5)
print(df)

result = pd.pivot_table(df, values='Population', columns='Capital')
print(result)

• Line [1] reads in a CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] outputs the DataFrame to the terminal.
• Line [4] creates a pivot table from the DataFrame based on the Population and Capital columns. The output saves to result.
• Line [5] outputs the result to the terminal.

Output:

df

result

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters. Notice the max function.

Code – Example 2

df = pd.read_csv('countries.csv')
df = df.head(5)

result = pd.pivot_table(df, values='Population', columns='Capital', aggfunc=[max])
print(result)

• Line [1] reads in a comma-separated CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] creates a pivot table from the DataFrame based on the Population and Capital columns. The max population is a parameter of aggfunc. The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

result

DataFrame reorder_levels()

The reorder_levels() method re-arranges the index of a DataFrame/Series. This method can not contain any duplicate level(s) or drop level(s).

The syntax for this method is as follows:

DataFrame.reorder_levels(order, axis=0)

For this example, there are five (5) students. Each student has some associated data with it. Grades generate by using np.random.randint().

index = [(1001, 'Micah Smith', 14), (1001, 'Philip Jones', 15), 
         	(1002, 'Ben Grimes', 16), (1002, 'Alicia Heath', 17), (1002, 'Arch Nelson', 18)]
m_index = pd.MultiIndex.from_tuples(index)
grades_lst = np.random.randint(45,100,size=5)
df = pd.DataFrame({"Grades": grades_lst}, index=m_index)
print(df)

result = df.reorder_levels([1,2,0])
print(result)

• Line [1] creates a List of tuples. Each tuple contains three (3) values. The output saves to index.
• Line [2] creates a MultiIndex from the List of Tuples created on line [1] and saves to m_index.
• Line [3] generates five (5) random grades between the specified range and saves to grades_lst.
• Line [4] creates a DataFrame from the variables on lines [1-3] and saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] re-orders the levels as specified. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

result

DataFrame sort_values()

The sort_values() method sorts (re-arranges) the elements of a DataFrame.

The syntax for this method is as follows:

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

For this example, a comma-delimited CSV file is read in. This DataFrame sorts on the Capital column in descending order.

df = pd.read_csv('countries.csv')
result = df.sort_values(by=['Capital'], ascending=False)
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sorts the DataFrame on the Capital column in descending order. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

DataFrame sort_index()

The sort_index() method sorts the DataFrame.

The syntax for this method is as follows:

DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

For this example, a comma-delimited CSV file is read into a DataFrame. This DataFrame sorts on the index Country column.

df = pd.read_csv('countries.csv')
df = df.set_index('Country')
result = df.sort_index()
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sets the index of the DataFrame to Country. The output saves to df (over-writing original df).
• Line [3] sorts the DataFrame (df) on the indexed column (Country) in ascending order (default). The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

Finxter

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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

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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

result

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

result

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

result

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

result

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')

Possible Errors Raised:

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:

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:

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

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MUO – Feed

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