Having trouble finding a way to get every two letter combination in a string in a dataframe. Everything I have been searching is for words rather than letters. Below is expected output. stringoutputhellohe, el, ll, loworldwo, or, rl, I have tried both lines below df[‘bigram’] = list(zip(df[‘string’],df[‘string][1:])) Generated this error ValueError: Length of values (15570)… Read More Two Letter Bigram in Pandas Dataframe

I’am pretty new here. I want to parse a table from wikipedia from a following link: https://en.wikipedia.org/wiki/MIUI I was able to parse first table, but I can’t figure out how to get the information from the second table there, the information that contains "version history" of MIUI browser. Is there a way to parse it… Read More Parse a table from wikipedia that is hidden

I am converting large numbers to int using astype(). import pandas as pd x = [56668772800.0, 55899926600.0, 55038007900.0, 58073681300.0, 58224458500.0] df = pd.DataFrame(x, columns=["pol_number"]) df.pol_number = df.pol_number.astype(int) df And all five values have become -2147483648. I assume the numbers are too big for type int but long int and big int don’t compile. >Solution :… Read More Pandas: astype(int) converts all values to -2147483648

Given a data frame that is sorted by symbol and date. I would like to add a new column that identifies each individual transaction. Each individual transaction means that the position is opened and closed after a set of buy and sell orders. Input data time symbol side price size 08-19-2021 AAPL buy 150 1… Read More How to group each individual transaction from a buy and sell history

Let’s say I have a DataFrame as data, and I want to find if every single column in the data frame is an object and use it as an if condition. example: describe = data.describe (if condition to find all the columns are ‘object’): agg = data.agg([‘a’,’b’,’c’]) if not agg.empty: describe = pd.concat(describe,agg) describe =… Read More How to find if all the columns in a dataframe are object dtype?

I have the following df df = pd.DataFrame( {‘id’:[1,1,1,2,2,2,3,3,3], ‘value’:[‘pot’,’pot’,’jebus’,’pot’,’jebus’,’pot’,’pot’,’jebus’,’jebus’]}) What I want to do is to identify if an id contains repetitive values but only if a row is followed by another row with the same value. So if I have ‘pot’ and after that ‘pot’ again, I want to flag both as true.… Read More Flag element in groupby if it is equal to his sucessor in next row

I’m writing a simple function to calculate the moving average. For instance, if there is a list [1, 2, 3, 7, 9], then the moving average with the window size of 3, assuming the length of the output is the same, should be [nan, nan, 2, 4, 6.33]. I searched the web and found that… Read More How to get the element of the output from the function pd.Series().rolling()?

I have this: df = name year. salary. d. a 1990. 3. 5 b 1992. 90. 1 c 1990. 234. 3 … I am trying to group my data frame based on year, and then get the average of the salaries in that year. Then my goal is to assign it to a new column.… Read More mean of a column after group by returns nan

Using the Selenium library, I get some information (a table from the site) and try to save this table to a CSV file. elements_xpach_url = browser.find_element(By.XPATH, ‘/html/body/div[2]/span[2]/table/tbody[2]’) values = re.split(‘\n’, elements_xpach_url.text) df = pd.DataFrame(values) df.to_csv(‘csv.csv’, index=False, header=False, sep=’;’) In the final file, there is no way to set the division as a semicolon ; instead… Read More Correctly save list to CSV file

I’m reading in all files within a folder and these files will be merged together when they share the same name in their filename. Now, I would like to add their specific-individual path v to an additional column while each time the files are read in as dataframes. Is this possible? This is the code:… Read More add column while reading in (pd.read_csv)