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How to select different sets of variables (ei value counts for a specific country) from a groupby df for a 2,2 subplot

From my original data frame, I used the group-by to create the new df as shown below, which has the natural disaster subtype counts for each country.
However, I'm unsure how to, for example, select 4 specific countries and set them as variables in a 2 by 2 plot.
The X-axis will be the disaster subtype name, with the Y being the value count, however, I can't quite figure out the right code to select this information.
This is how I grouped the countries -
g_grp= df_geo.groupby(['Country'])
c_val = pd.DataFrame(c_grp['Disaster Subtype'].value_counts())
c_val = c_val.rename(columns={'Disaster Subtype': 'Disaster Subtype', 'Disaster Subtype': 'Num of Disaster'})
c_val.head(40)
Output:
Country Disaster Subtype
Afghanistan Riverine flood 45
Ground movement 33
Flash flood 32
Avalanche 19
Drought 8
Bacterial disease 7
Convective storm 6
Landslide 6
Cold wave 5
Viral disease 5
Mudslide 3
Severe winter conditions 2
Forest fire 1
Locust 1
Parasitic disease 1
Albania Ground movement 16
Riverine flood 8
Severe winter conditions 3
Convective storm 2
Flash flood 2
Heat wave 2
Avalanche 1
Coastal flood 1
Drought 1
Forest fire 1
Viral disease 1
Algeria Ground movement 21
Riverine flood 20
Flash flood 8
Bacterial disease 2
Cold wave 2
Forest fire 2
Coastal flood 1
Drought 1
Heat wave 1
Landslide 1
Locust 1
American Samoa Tropical cyclone 4
Flash flood 1
Tsunami 1
However, let's say I want to select these for and plot 4 plots, 1 for each country, showing the number of each type of disaster happening in each country, I know I would need something along the lines of what's below, but I'm unsure how to set the x and y variables for each -- or if there is a more efficient way to set the variables/plot, that would be great. Usually, I would just use loc or iloc, but I need to be more specific with selecting.
fig, ax = subplots(2,2, figsize(16,10)
X1 = c_val.loc['Country'] == 'Afghanistan' #This doesn't work, just need something similar
y1 = c_val.loc['Num of Disasters']
X2 =
y2 =
X3 =
y3 =
X4 =
y4 =
ax[0,0].bar(X1,y1,width=.4, color=['#A2BDF2'])
ax[0,1].bar(X2,y2,width=.4,color=['#A2BDF2'])
ax[1,0].bar(X3,y3,width=.4,color=['#A2BDF2'])
ax[1,1].bar(X4,y4,width=.4,color=['#A2BDF2'])

IIUC, an simple way is to use catplot from seaborn package:
# Python env: pip install seaborn
# Anaconda env: conda install seaborn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
g = sns.catplot(x='Disaster Subtype', y='Num of Disaster', col='Country',
data=df, col_wrap=2, kind='bar')
g.set_xticklabels(rotation=90)
g.tight_layout()
plt.show()
Update
How I can select the specific countries to be plotted in each subplot?
subdf = df.loc[df['Country'].isin(['Albania', 'Algeria'])]
g = sns.catplot(x='Disaster Subtype', y='Num of Disaster', col='Country',
data=subdf, col_wrap=2, kind='bar')
...

how to deal with a copy-pasted table in pandas- reshaping a column vector

I have a table I copied from a webpage which when pasted into librecalc or excel occupies a single cell, and when pasted into notebook becomes a 3507x1 column. If I import this as a pandas dataframe using pd.read_csv I see the same 3507x1 column , and I'd now like to reshape it into the 501x7 array that it started as.
I thought I could recast as a numpy array, reshape as I am familiar with in numpy and then put back into a df, but the to_numpy methods of pandas seem to want to work with a Series object (not Dataframe) and attempts to read the file into a Series using eg
ser= pd.Series.from_csv('billionaires')
led to tokenizing errors. Is there some simple way to do this? Maybe I should throw in the towel on this direction and read from the html?

A simple copy paste does not give you any clear column separator, so it's impossible to do it easily.
You have only spaces, but spaces may or may not be inside the column values too (like in the name or country) so is impossible to give to DataFrame.read_csv a column separator.
However, if I copy paste the table in a file, I notice regularity.
If you know regex, you can try using pandas.Series.str.extract. This method extracts capture groups in a regex pattern as columns of a DataFrame. The regex is applied to each element / string of the series.
You can then try to find a regex pattern to capture the various elements of the row to split them into separate columns.
df = pd.read_csv('data.txt', names=["A"]) #no header in the file
ss = df['A']
rdf = ss.str.extract('(\d)\s+(.+)(\$[\d\.]+B)\s+([+-]\$[\d\.]+[BM])\s+([+-]\$[\d\.]+B)\s+([\w\s]+)\s+([\w\s]+)')
Here I tried to write a regex for the table in the link, the result on the first seems pretty good.
0 1 2 3 4 5 6
0 1 Jeff Bezos $121B +$231M -$3.94B United States Technology
1 3 Bernard Arnault $104B +$127M +$35.7B France Consumer
2 4 Warren Buffett $84.9B +$66.3M +$1.11B United States Diversified
3 5 Mark Zuckerberg $76.7B -$301M +$24.6B United States Technology
4 6 Amancio Ortega $66.5B +$303M +$7.85B Spain Retail
5 7 Larry Ellison $62.3B +$358M +$13.0B United States Technology
6 8 Carlos Slim $57.0B -$331M +$2.20B Mexico Diversified
7 9 Francoise Bettencourt Meyers $56.7B -$1.12B +$10.5B France Consumer
8 0 Larry Page $55.7B +$393M +$4.47B United States Technology
I used DataFrame.read_csv to read the file, since `Series.from_csv' is deprecated.

I found that converting to a numpy array was far easier than I had realized - the numpy asarray method can handle a df (and conveniently enough it works for general objects, not just numbers)
df = pd.read_csv('billionaires',sep='\n')
print(df.shape)
-> (3507, 1)
n = np.asarray(df)
m = np.reshape(n,[-1,7])
df2=pd.DataFrame(m)
df2.head()
0 1 2 3 4 \
0 0 Name Total net worth $ Last change $ YTD change
1 1 Jeff Bezos $121B +$231M -$3.94B
2 2 Bill Gates $107B -$421M +$16.7B
3 3 Bernard Arnault $104B +$127M +$35.7B
4 4 Warren Buffett $84.9B +$66.3M +$1.11B
5 6
0 Country Industry
1 United States Technology
2 United States Technology
3 France Consumer
4 United States Diversified

Python remove row if cell value in dataframe contain characters less than 5

I have a dataframe like I am trying to keep rows that have more than 5 characters. Here is what I tried, but it removes 'of', 'U.', 'and','Arts',...etc. I just need to remove characters in a row that have len less than 5.
id schools
1 University of Hawaii
2 Dept in Colorado U.
3 Dept
4 College of Arts and Science
5 Dept
6 Bldg
wrong output from my code:
0 University Hawaii
1 Colorado
2
3 College Science
4
5
Looking for output like this:
id schools
1 University of Hawaii
2 Dept in Colorado U.
4 College of Arts and Science
Code:
l = [1,2,3,4,5,6]
s = ['University of Hawaii', 'Dept in Colorado U.','Dept','College of Arts and Science','Dept','Bldg']
df1 = pd.DataFrame({'id':l, 'schools':s})
df1 = df1['schools'].str.findall('\w{5,}').str.join(' ') # not working
df1

Using a regex is a huge (and slow) overkill for this task. You can use simple pandas indexing:
filtrered_df = df1[df1['schools'].str.len() > 5] # or >= depending on the required logic

There is a simpler filter for your data.
mask = df1['schools'].str.len() > 5
Then create a new data frame from the filter
df2 = df1[mask].copy()

import pandas as pd
name = ['University of Hawaii','Dept in Colorado U.','Dept','College of Arts and Science','Dept','Bldg']
labels =['schools']
df =pd.DataFrame.from_records([[i] for i in name],columns=labels)
df[df['schools'].str.len() >5 ]

Boxplot: Extract outliers and tag them as either '0' or '1'

I'm trying to extract outliers from my dataset and tag them accordingly.
Sample Data
Doctor Name Hospital Assigned Region Claims Illness Claimed
1 Albert Some hospital Center R-1 20 Sepsis
2 Simon Another hospital Center R-2 21 Pneumonia
3 Alvin ... ... ... ...
4 Robert
5 Benedict
6 Cruz
So I'm trying to group every Doctor that Claimed a certain Illness in a certain Region and trying to find outliers among them.
Doctor Name Hospital Assigned Region Claims Illness Claimed is_outlier
1 Albert Some hospital Center R-1 20 Sepsis 1
2 Simon Another hospital Center R-2 21 Pneumonia 0
3 Alvin ... ... ... ...
4 Robert
5 Benedict
6 Cruz
I can do this in Power BI. But being fairly new to Python, I can't seem to figure this out.
This is what I'm trying to achieve:
Algo goes like:
Read data
Group data by Illness
Group by Region
get IQR based on Claims Count
if claims count > than (Q3 + 1.5) * IQR
then tag it as outlier = 1
else
not an outlier = 0
Export data
Any ideas?

Assuming you use pandas for data analysis (and you should!) You can use pandas dataframe boxplot to produce a plot similar to yours:
import pandas as pd
import numpy as np
df.boxplot(column=['b'], whis=[10, 90], vert=False,
flierprops=dict(markerfacecolor='g', marker='D'))
or, if you want to mark them 0,1 as you requested, use dataframe quantile() method https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html
df.assign(outlier=df[df>=df.quantile(.9)].any(axis=1)).astype(np.int8)
a b outlier
0 1 1 0
1 2 10 0
2 3 100 1
3 4 100 1

nested for loops with pandas dataframe

I am looping through a dataframe column of headlines (sp500news) and comparing against a dataframe of company names (co_names_df). I am trying to update the frequency each time a company name appears in a headline.
My current code is below and is not updating the frequency columns. Is there a cleaner, faster implementation - maybe without the for loops?
for title in sp500news['title']:
for string in title:
for co_name in co_names_df['Name']:
if string == co_name:
co_names_index = co_names_df.loc[co_names_df['Name']=='string'].index
co_names_df['Frequency'][co_names_index] += 1
co_names_df sample
Name Frequency
0 3M 0
1 A.O. Smith 0
2 Abbott 0
3 AbbVie 0
4 Accenture 0
5 Activision 0
6 Acuity Brands 0
7 Adobe Systems 0
...
sp500news['title'] sample
title
0 Italy will not dismantle Montis labour reform minister
1 Exclusive US agency FinCEN rejected veterans in bid to hire lawyers
4 Xis campaign to draw people back to graying rural China faces uphill battle
6 Romney begins to win over conservatives
8 Oregon mall shooting survivor in serious condition
9 Polands PGNiG to sign another deal for LNG supplies from US CEO

You can probably speed this up; you're using dataframes where other structures would work better. Here's what I would try.
from collections import Counter
counts = Counter()
# checking membership in a set is very fast (O(1))
company_names = set(co_names_df["Name"])
for title in sp500news['title']:
for word in title: # did you mean title.split(" ")? or is title a list of strings?
if word in company_names:
counts.update([word])
counts is then a dictionary {company_name: count}. You can just do a quick loop over the elements to update the counts in your dataframe.