Develop Reference

Correct way to iterate over two dataframes to set specific values based on the value of another df

Edited to add easier to reproduce dataframe
I have two dataframes that look something like this:
df1
index = [0,1,2,3,4,5,6,7,8]
a = pd.Series([John Smith, John Smith, John Smith, Kobe Bryant, Kobe Bryant, Kobe Bryant, Jeff Daniels, Jeff Daniels, Jeff Daniels],index= index)
b = pd.Series([7/29/2022, 8/7/2022, 8/29/2022, 7/9/2022, 7/29/2022, 8/9/2022, 7/28/2022, 8/8/2022, 8/28/2022],index= index)
c = pd.Series([185, 187, 186.5, 212.5, 217.5, 220.5, 211.1, 210.5, 213],index= index)
d = pd.Series([],index= index)
df1 = pd.DataFrame(np.d_[a,b,c],columns = ["Name","Date","Weight","Goal"])
or df1 in this format:
Name
Date
Weight
Goal
John Smith
7/29/2022
185
NaN
John Smith
8/7/2022
187
NaN
John Smith
8/29/2022
186.5
NaN
Kobe Bryant
7/9/2022
212.5
NaN
Kobe Bryant
7/29/2022
217.5
NaN
Kobe Bryant
8/9/2022
220.5
NaN
Jeff Daniels
7/28/2022
211.1
NaN
Jeff Daniels
8/8/2022
210.5
NaN
Jeff Daniels
8/28/2022
213
NaN
df2
index = [0,1,2]
a = pd.Series([John Smith, Kobe Bryant, Jeff Daniels],index= index)
b = pd.Series([195,230,220],index= index)
c = pd.Series([],index= index)
df2 = pd.DataFrame(np.c_[a,b],columns = ["Name", "Weight Goal"])
or df2 in this format:
Name
Weight Goal
John Smith
195
Kobe Bryant
230
Jeff Daniels
220
What I want to do is iterate through df1 and set respective weight goal from df2 for each player...but I only want to do this in August, I want to ignore the July dates.
I know that I shouldn't be using a for loop with a dataframe/pandas but I think me showing my mental thought process with one might show the intent that I was trying to achieve with my code attempts.
for player in df1['Name']:
df1 = df1.loc[(df1['Name'] == f'{player}') & (df1['Date'] > '8/1/2022')]
df1.at[df2['Name'] == f'{player}', 'Goal'] = (df2.loc[df2.Name == f'{player}']['Weight Goal'])
This just ends up delivering an empty dataframe & a settingwithcopy warning. I know this is not the right way to do this but I thought it might help to direct me.
Thank You.

If I correctly understand the output you are after (stack overflow tip: it can be useful to provide a sample of your desired output to help people trying to answer your question), then this should work:
# make the Date column into datetime type so it is easier to filter on
df1 = df1.assign(Date=pd.to_datetime(df1.Date))
# separate out the august rows from the other months
df1_august = df1.loc[df1.Date.apply(lambda x: x.month == 8)]
df1_other_months = df1.loc[df1.Date.apply(lambda x: x.month != 8)]
# use a merge rather than a loop to get WeightGoal column in place
df1_august_merged = df1_august.merge(df2, on="Name")
# finally add the rows for the other months back in
final_df = pd.concat([df1_august_merged, df1_other_months])
print(final_df)
Name Date Weight Goal Weight Goal
0 John Smith 2022-08-07 187.0 NaN 195.0
1 John Smith 2022-08-29 186.5 NaN 195.0
2 Kobe Bryant 2022-08-09 220.5 NaN 230.0
3 Jeff Daniels 2022-08-08 210.5 NaN 220.0
4 Jeff Daniels 2022-08-28 213.0 NaN 220.0
0 John Smith 2022-07-29 185.0 NaN NaN
3 Kobe Bryant 2022-07-09 212.5 NaN NaN
4 Kobe Bryant 2022-07-29 217.5 NaN NaN
6 Jeff Daniels 2022-07-28 211.1 NaN NaN

How to grab contained space when scraping python using spider

Can anybody help, please?
I am finding it difficult to grab the value of currency units per SDR using my python code below.
The code is working with other URLs, but for this URL, I always get a null result. I don’t understand what is wrong.
I’m using python scrappy spider.
URL: https://www.imf.org/external/np/fin/data/rms_five.aspx
I reviewed the content on the website and found that it contains some spaces. There are some spaces in the element value:
Using RSS response and XPath, I get the same result i.e. null
def start_requests(self):
urls = [
'https://www.imf.org/external/np/fin/data/rms_five.aspx'
]
for url in urls:
yield scrapy.Request(url=url, callback=self.parse)
def parse(self, response):
print(response.url)
#for i in range (1, 24):
yield {
'Kurs_imf2': response.xpath('//*[#id="content"]/center/table/tbody/tr[3]/td/div/table/tbody/tr[3]/td[3]/text()').getall()
}

Well, looking only at your ultimate goal (and disregarding your eventual unfortunate choice of tools), here is one way of getting the data from that table, as a dataframe. Once you have the data, you can manipulate it as needed (like stripping the leading/trailing spaces, etc):
import requests
import pandas as pd
from bs4 import BeautifulSoup as bs
headers = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.5112.79 Safari/537.36'
}
url = 'https://www.imf.org/external/np/fin/data/rms_five.aspx'
r = requests.get(url, headers=headers)
soup = bs(r.text, 'html.parser')
table_w_data = soup.select_one('div.fancy').select_one('table')
df = pd.read_html(str(table_w_data))[0]
print(df)
This will display in terminal:
('SDRs per Currency unit 2', 'Unnamed: 0_level_1')
('SDRs per Currency unit 2', 'September 05, 2022')
('SDRs per Currency unit 2', 'September 02, 2022')
('SDRs per Currency unit 2', 'September 01, 2022')
('SDRs per Currency unit 2', 'August 31, 2022')
('SDRs per Currency unit 2', 'August 30, 2022')
0
Chinese yuan
nan
0.111437
0.111266
0.111474
0.110906
1
Euro
nan
0.768586
0.768411
0.768436
0.769353
2
Japanese yen
nan
0.00549178
0.00550612
0.00554387
0.00553607
3
U.K. pound
nan
0.889646
0.887851
0.892615
0.898473
4
U.S. dollar
nan
0.769124
0.768104
0.768436
0.766746
5
Algerian dinar
nan
0.0054812
0.00547939
0.00547416
0.00547027
6
Australian dollar
nan
0.522235
0.524922
0.530375
0.529438
7
Botswana pula
nan
0.0593764
0.0595281
0.0600917
0.0600362
8
Brazilian real
nan
0.148273
0.147709
0.148393
0.151498
9
Brunei dollar
nan
0.548669
0.548215
0.55085
0.54893
10
Canadian dollar
nan
0.586178
0.5834
0.5861
0.586377
11
Chilean peso
nan
0.000856257
0.000855312
0.000871134
0.000860642
12
Czech koruna
nan
0.0313774
0.0313768
0.0313289
0.0312983
13
Danish krone
nan
0.103346
0.10332
0.103325
0.103441
14
Indian rupee
nan
0.0096395
0.00967422
nan
0.00961806
15
Israeli New Shekel
nan
0.227889
0.228331
0.230002
0.231996
16
Korean won
nan
0.000569131
0.000571039
0.000570268
0.000568929
17
Kuwaiti dinar
nan
nan
2.49425
2.49654
2.49105
18
Malaysian ringgit
nan
0.171526
0.171356
nan
0.170977
19
Mauritian rupee
nan
0.0172087
nan
0.0171443
0.0171741
20
Mexican peso
nan
0.0385038
0.0379361
0.0382379
0.0380585
21
New Zealand dollar
nan
0.466781
0.468236
0.471167
0.47105
22
Norwegian krone
nan
0.0768317
0.0767413
0.0773168
0.0788655
23
Omani rial
nan
nan
1.99767
1.99853
1.99414
24
Peruvian sol
nan
0.198946
0.199042
0.200166
nan
25
Philippine peso
nan
nan
0.0136744
0.0136628
0.0136826
26
Polish zloty
nan
0.162688
0.163569
0.162254
0.162412
27
Qatari riyal
nan
nan
0.211018
0.211109
0.210645
28
Russian ruble
nan
0.0127399
0.0127514
0.0127565
0.0127013
29
Saudi Arabian riyal
nan
nan
0.204828
0.204916
0.204466
30
Singapore dollar
nan
0.548669
0.548215
0.55085
0.54893
31
South African rand
nan
0.0444661
0.0448438
0.0450817
0.0455618
32
Swedish krona
nan
0.0715711
0.0718351
0.0720278
0.0718782
33
Swiss franc
nan
0.782903
0.78458
0.784038
0.789442
34
Thai baht
nan
0.0208978
0.020927
0.0210548
0.0210465
35
Trinidadian dollar
nan
0.114475
0.114048
nan
0.114091
36
U.A.E. dirham
nan
nan
0.20915
0.209241
0.20878
37
Uruguayan peso
nan
0.0188128
0.0188288
0.0187606
0.0188043
​
Relevant documentation:
Requests: https://requests.readthedocs.io/en/latest/
Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_html.html
BeautifulSoup: https://beautiful-soup-4.readthedocs.io/en/latest/index.html

Need to remove nan from a column when its concatenated with strings using pandas [duplicate]

This question already has answers here:
pandas combine two strings ignore nan values
(5 answers)
Closed 11 months ago.
My data looks like this:
First_Name Middle_Name Last_Name Full_Name (Header)
John R Rovin John R Rovin
Marano Justine Marano nan Justine
David Rose David nan Rose
nan nan nan
Robert Robert nan nan
I am trying to trim nan from Full_Name column to just get whatever name it possibly contains as is. When I am trying to use Fillna(' ') , its not helping me to trim but its completely removing the column content. My final DF should look something like this:
First_Name Middle_Name Last_Name Full_Name (Header)
John R Rovin John R Rovin
Marano Justine Marano Justine
David Rose David Rose
Robert Robert
I am heavily dependent for most of the operations on Pandas. So is there any way using pandas I can solve this problem?

You can use:
cols = ['First_Name', 'Middle_Name', 'Last_Name']
df['Full_Name'] = df[cols].apply(lambda x: ' '.join(i for i in x if pd.notna(i)), axis=1)
print(df)
# Output
First_Name Middle_Name Last_Name Full_Name
0 John R Rovin John R Rovin
1 Marano NaN Justine Marano Justine
2 David NaN Rose David Rose
3 NaN NaN NaN
4 Robert NaN NaN Robert
Setup a MRE
import pandas as pd
import numpy as np
data = {'First_Name': ['John', 'Marano', 'David', np.nan, 'Robert'],
'Middle_Name': ['R', np.nan, np.nan, np.nan, np.nan],
'Last_Name': ['Rovin', 'Justine', 'Rose', np.nan, np.nan]}
df = pd.DataFrame(data)

if nan always appear the same in Full_Name column you can use this:
df['Full_Name (Header)'] = df['Full_Name (Header)'].str.replace('nan', ' ')

Use Series.dropna for remove misisng values before join:
cols = ['First_Name', 'Middle_Name', 'Last_Name']
df['Full_Name'] = df[cols].apply(lambda x: ' '.join(x.dropna()), axis=1)
print(df)
First_Name Middle_Name Last_Name Full_Name
0 John R Rovin John R Rovin
1 Marano NaN Justine Marano Justine
2 David NaN Rose David Rose
3 NaN NaN NaN
4 Robert NaN NaN Robert

How to Insert following beautiful soup scraped data in excel?

from bs4 import BeautifulSoup
import pandas as pd
import requests
import time
from datetime import datetime
def extract_source(url):
agent = {"User-Agent":"Mozilla/5.0"}
source=requests.get(url, headers=agent).text
return source
html_text = extract_source('https://www.mpbio.com/us/life-sciences/biochemicals/amino-acids')
soup = BeautifulSoup(html_text, 'lxml')
for a in soup.find_all('a', class_ = 'button button--link button--fluid catalog-list-item__actions-primary-button', href=True):
# print ("Found the URL:", a['href'])
urlof = a['href']
html_text = extract_source(urlof)
soup = BeautifulSoup(html_text, 'lxml')
table_rows = soup.find_all('tr')
first_columns = []
third_columns = []
for row in table_rows:
# for row in table_rows[1:]:
first_columns.append(row.findAll('td')[0])
third_columns.append(row.findAll('td')[1])
for first, third in zip(first_columns, third_columns):
print(first.text, third.text)
Basically I am trying to scrape data from tables from multiple links of Website. And I want to insert that data in one excel csv file in following table format
SKU 07DE9922
Analyte / Target Corticosterone
Base Catalog Number DE9922
Diagnostic Platforms EIA/ELISA
Diagnostic Solutions Endocrinology
Disease Screened Corticosterone
Evaluation Quantitative
Pack Size 96 Wells
Sample Type Plasma, Serum
Sample Volume 10 uL
Species Reactivity Mouse, Rat
Usage Statement For Research Use Only, not for use in diagnostic procedures.
To below format in excel file
SKU Analyte/Target Base Catalog Number Pack Size Sample Type
data data data data data
I am facing difficulties while converting data in proper format

I made small modification to your code. Instead of printing the data, I created a dictionary and added it to list. Then I used this list to create a DataFrame:
import pandas as pd
import requests
import time
from datetime import datetime
def extract_source(url):
agent = {"User-Agent": "Mozilla/5.0"}
source = requests.get(url, headers=agent).text
return source
html_text = extract_source(
"https://www.mpbio.com/us/life-sciences/biochemicals/amino-acids"
)
soup = BeautifulSoup(html_text, "lxml")
data = []
for a in soup.find_all(
"a",
class_="button button--link button--fluid catalog-list-item__actions-primary-button",
href=True,
):
urlof = a["href"]
html_text = extract_source(urlof)
soup = BeautifulSoup(html_text, "lxml")
table_rows = soup.find_all("tr")
first_columns = []
third_columns = []
for row in table_rows:
first_columns.append(row.findAll("td")[0])
third_columns.append(row.findAll("td")[1])
# create dictionary with values and add to the list
d = {}
for first, third in zip(first_columns, third_columns):
d[first.get_text(strip=True)] = third.get_text(strip=True)
data.append(d)
df = pd.DataFrame(data)
print(df)
df.to_csv("data.csv", index=False)
Prints:
SKU Alternate Names Base Catalog Number CAS # EC Number Format Molecular Formula Molecular Weight Personal Protective Equipment Usage Statement Application Notes Beilstein Registry Number Optical Rotation Purity UV Visible Absorbance Hazard Statements RTECS Number Safety Symbol Auto Ignition Biochemical Physiological Actions Density Melting Point pH pKa Solubility Vapor Pressure Grade Boiling Point Isoelectric Point
0 02100078-CF 2-Acetamido-5-Guanidinovaleric acid 100078 210545-23-6 205-846-6 Powder C8H16N4O3· 2H2O 216.241 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 02100142-CF Acetyltryptophan; DL-α-Acetylamino-3-indolepro... 100142 87-32-1 201-739-3 Powder C13H14N2O3 246.266 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... N-acetyl-DL-tryptophan, is used as stabilizer ... 89478 0° ± 2° (c=1, 1N NaOH, 24 hrs.) ~99% λ max (water)=280 ± 2 nm NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 02100421-CF L-2,5-Diaminopentanoic acid; 2,5-Diaminopentan... 100421 3184-13-2 221-678-6 Powder C5H12N2O2 • HCl 168.621 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... NaN 3625847 NaN ~99% NaN H319 RM2985000 GHS07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 02100520-CF Phosphocreatine Disodium Salt Tetrahydrate; So... 100520 922-32-7 213-074-6 Powder C4H8N3Na2O5P·4H2O 255.077 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... NaN NaN NaN ≥98% NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 02100769-CF Vitamin C; Ascorbate; Sodium ascorbate; L-Xylo... 100769 50-81-7 200-066-2 NaN C6H8O6 176.124 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... L-Ascorbic Acid is used as an Antimicrobial an... 84272 +18° to +32° (c=1, water) ≥98% NaN NaN CI7650000 NaN 1220° F (NTP, 1992) Ascorbic Acid, also known as Vitamin C, is a s... 1.65 (NTP, 1992) 374 to 378° F (decomposes) (NTP, 1992) Between 2,4 and 2,8 (2 % aqueous solution) pK1: 4.17; pK2: 11.57 greater than or equal to 100 mg/mL at 73° F (... 9.28X10-11 mm Hg at 25 deg C (est) NaN NaN NaN
5 02101003-CF Lycine; Oxyneurine; (Carboxymethyl)trimethylam... 101003 107-43-7 203-490-6 Powder C5H11NO2 117.148 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... Betaine is a reagent that is used in soldering... 3537113 NaN NaN NaN NaN DS5900000 NaN NaN End-product of oxidative metabolism of choline... NaN Decomposes around 293 deg C NaN 1.83 (Lit.) Solubility (g/100 g solvent): <a class="pubche... 1.36X10-8 mm Hg at 25 deg C (est) Anhydrous NaN NaN
6 02101806-CF (S)-2,5-Diamino-5-oxopentanoic acid; L-Glutami... 101806 56-85-9 200-292-1 NaN C5H10N2O3 146.146 g/mol Eyeshields, Gloves,  respirator filter Unless specified otherwise, MP Biomedical's pr... L-glutamine is an essential amino acid, which ... 1723797 +30 ± 5° (c = 3.5, 1N HCl) ≥99% NaN NaN MA2275100 NaN NaN L-Glutamine is an essential amino acid that is... 1.364 g/cu cm 185.5 dec °C pH = 5-6 at 14.6 g/L at 25 deg C NaN Water Solubility41300 mg/L (at 25 °C) 1.9X10-8 mm Hg at 25 deg C (est) NaN NaN NaN
7 02102158-CF L-2-Amino-4-methylpentanoic acid; Leu; L; α-am... 102158 61-90-5 200-522-0 Powder C6H13NO2 131.175 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... Leucine has been used as a molecular marker in... 1721722 +14.5 ° to +16.5 ° (Lit.) NaN NaN NaN OH2850000 NaN NaN NaN 1.293 g/cu cm at 18 deg C 293 °C NaN 2.33 (-COOH), 9.74 (-NH2)(Lit.) Water Solubility21500 mg/L (at 25 °C) 5.52X10-9 mm Hg at 25 deg C (est) NaN Sublimes at 145-148 deg C. Decomposes at 293-2... 6.04(Lit.)
8 02102576-CF 4-Hydroxycinnamic acid; 3-(4-Hydroxphenyl)-2-p... 102576 7400-08-0 231-000-0 Powder C9H8O3 164.16 g/mol Dust mask, Eyeshields, Gloves Unless specified otherwise, MP Biomedical's pr... p-Coumaric acid was used as a substrate to stu... NaN NaN ≥98% NaN NaN GD9094000 NaN NaN NaN NaN 211.5 °C NaN NaN NaN NaN NaN NaN NaN
9 02102868-CF DL-2-Amino-3-hydroxypropionic acid; (±)-2-Amin... 102868 302-84-1 206-130-6 Powder C3H7NO3 105.093 g/mol Eyeshields, Gloves, respirator filter Unless specified otherwise, MP Biomedical's pr... NaN 1721405 -1° to + 1° (c = 5, 1N HCl) ≥98% NaN NaN NaN NaN NaN NMDA agonist acting at the glycine site; precu... 1.6 g/cu cm # 22 deg C 228 deg C (decomposes) NaN NaN SOL IN <a class="pubchem-internal-link CID-962... NaN NaN NaN NaN
...and so on.
And saves data.csv (screenshot from LibreOffice):

Try this -
import pandas as pd
import requests
import time
from datetime import datetime
from bs4 import BeautifulSoup
def extract_source(url):
agent = {"User-Agent": "Mozilla/5.0"}
return requests.get(url, headers=agent).text
html_text = extract_source(
'https://www.mpbio.com/us/life-sciences/biochemicals/amino-acids')
soup = BeautifulSoup(html_text, 'lxml')
result = []
for index, a in enumerate(soup.find_all('a', class_='button button--link button--fluid catalog-list-item__actions-primary-button', href=True)):
# if index >= 10:
# break
# print ("Found the URL:", a['href'])
urlof = a['href']
html_text = extract_source(urlof)
soup = BeautifulSoup(html_text, 'lxml')
table_rows = soup.find_all('tr')
first_columns = []
third_columns = []
for row in table_rows:
# for row in table_rows[1:]:
first_columns.append(row.findAll('td')[0])
third_columns.append(row.findAll('td')[1])
temp = {}
for first, third in zip(first_columns, third_columns):
third = str(third.text).strip('\n')
first = str(first.text).strip('\n')
temp[first] = third
result.append(temp)
df = pd.DataFrame(result)
# please drop useless column before saving the output to csv.
df.to_csv('out.csv', index=False)
This will give output -
SKU
Alternate Names
Base Catalog Number
CAS #
EC Number
Format
Molecular Formula
Molecular Weight
Personal Protective Equipment
Usage Statement
Application Notes
Beilstein Registry Number
Optical Rotation
Purity
UV Visible Absorbance
Hazard Statements
RTECS Number
Safety Symbol
02100078-CF
2-Acetamido-5-Guanidinovaleric acid
100078
210545-23-6
205-846-6
Powder
C8H16N4O3· 2H2O
216.241 g/mol
Eyeshields, Gloves, respirator filter
Unless specified otherwise, MP Biomedical's products are for research or further manufacturing use only, not for direct human use. For more information, please contact our customer service department.
02100142-CF
Acetyltryptophan; DL-α-Acetylamino-3-indolepropionic acid
100142
87-32-1
201-739-3
Powder
C13H14N2O3
246.266 g/mol
Eyeshields, Gloves, respirator filter
Unless specified otherwise, MP Biomedical's products are for research or further manufacturing use only, not for direct human use. For more information, please contact our customer service department.
N-acetyl-DL-tryptophan, is used as stabilizer in the human blood-derived therapeutic products normal serum albumin and plasma protein fraction.
89478
0° ± 2° (c=1, 1N NaOH, 24 hrs.)
~99%
λ max (water)=280 ± 2 nm
02100421-CF
L-2,5-Diaminopentanoic acid; 2,5-Diaminopentanoic acid monohydrochloride
100421
3184-13-2
221-678-6
Powder
C5H12N2O2 • HCl
168.621 g/mol
Eyeshields, Gloves, respirator filter
Unless specified otherwise, MP Biomedical's products are for research or further manufacturing use only, not for direct human use. For more information, please contact our customer service department.
3625847
~99%
H319
RM2985000
GHS07

Extract country name from text in column to create another column

I have tried different combinations to extract the country names from a column and create a new column with solely the countries. I can do it for selected rows i.e. df.address[9998] but not for the whole column.
import pycountry
Cntr = []
for country in pycountry.countries:
for country.name in df.address:
Cntr.append(country.name)
Any ideas what is going wrong here?
edit:
address is an object in the df and
df.address[:10] looks like this
Address
0 Turin, Italy
1 NaN
2 Zurich, Switzerland
3 NaN
4 Glyfada, Greece
5 Frosinone, Italy
6 Dublin, Ireland
7 NaN
8 Turin, Italy
1 NaN
2 Zurich, Switzerland
3 NaN
4 Glyfada, Greece
5 Frosinone, Italy
6 Dublin, Ireland
7 NaN
8 ...
9 Kristiansand, Norway
Name: address, Length: 10, dtype: object
Based on Petar's response when I run individual queries I get the country correctly, but when I try to create a column with all the countries (or ranges like df.address[:5] I get an empty Cntr)
import pycountry
Cntr = []
for country in pycountry.countries:
if country.name in df['address'][1]:
Cntr.append(country.name)
Cntr
Returns
[Italy]
and df.address[2] returns [ ]
etc.
I have also run
df['address'] = df['address'].astype('str')
to make sure that there are no floats or int in the column.

Sample dataframe
df = pd.DataFrame({'address': ['Turin, Italy', np.nan, 'Zurich, Switzerland', np.nan, 'Glyfada, greece']})
df[['city', 'country']] = df['address'].str.split(',', expand=True, n=2)
address city country
0 Turin, Italy Turin Italy
1 NaN NaN NaN
2 Zurich, Switzerland Zurich Switzerland
3 NaN NaN NaN
4 Glyfada, greece Glyfada greece

You were really close. We cannot loop like this for country.name in df.address. Instead:
import pycountry
Cntr = []
for country in pycountry.countries:
if country.name in df.address:
Cntr.append(country.name)
If this does not work, please supply more information because I am unsure what df.address looks like.

You can use the function clean_country() from the library DataPrep. Install it with pip install dataprep.
from dataprep.clean import clean_country
df = pd.DataFrame({"address": ["Turin, Italy", np.nan, "Zurich, Switzerland", np.nan, "Glyfada, Greece"]})
df2 = clean_country(df, "address")
df2
address address_clean
0 Turin, Italy Italy
1 NaN NaN
2 Zurich, Switzerland Switzerland
3 NaN NaN
4 Glyfada, Greece Greece