Having trouble loading a JSON file with pandas as expected! I've checked various Stack Overflow answers but my issue doesn't seem to be there. The structure of the JSON file is shown below: View JSON File Code snippet used to load the file:- import panda ...
My goal is to extract information from a JSON response and convert it into a dataframe for export to a .csv file. The JSON response structure includes the following fields: { "count":2, "next":null, "previous":null, "results":[ { ...
Let's consider a df structured as follows: stringOfInterest trend 0 C up 1 D down 2 E down 3 C,O up 4 C,P ...
If you have a pandas dataframe containing a timeseries in the format below: Date value 2020-01 1 2020-02 2 2020-03 3 You may want to convert this into a datetime series efficiently using a method like pd.to_datetime. This conversion process is strai ...
I am in possession of a dataset containing information about the Olympic games. My goal is to determine the total number of medals won (Gold/Silver/Bronze) for all sports in a particular country. In the case of Germany, ...
I am encountering a problem with the pandas package. Despite having numpy 1.9.0 and dateutil 2.5.0 installed using the command pip install python-dateutil==2.5.0, I am still receiving an error. Is there an alternative method to install dateutil that woul ...
Currently, I am dealing with a data frame that is in wide format. In this data frame, each book has a specific number of sales recorded. However, there are some quarters where the values are null because the book was not released before that particular qua ...
After successfully creating a dataframe with two columns using the pd.DataFrame method, I am curious if it is possible to modify the method to accommodate three columns instead. quantities = dict() quotes = dict() for index, row in df.iterrows(): # ...
https://i.stack.imgur.com/0Pe5x.png score represents the score attained on each delivery, while runs are the cumulative of these scores. The sequence consists of 6 deliveries with specified length/type for each over. I am aiming to calculate the average s ...
I have come across a few similar questions, but none of them seem to address my specific issue. My goal is simple - I want to use rolling.min with a variable window length from another column in the dataframe. Since my dataset may grow quite large in the ...
I currently have a pandas dataframe containing the following data: source ACCESS CREATED TERMS SIGNED BUREAU Facebook 12 8 6 Google 160 136 121 Email 29 26 25 While this is just a snippet of the dataframe, it showcases the various rows and col ...
I have a dataset structured like this: id Voltage Temperature1 Temperature2 0 A8404181D1822E6B 2.985 16.25 16.03 1 A84041A3A1822FE5 2.982 7.06 16.28 2 A8404181D1822E6B 2.985 16.31 16 ...
I am working with a dataset and have identified outliers that are 3 standard deviations away from the mean in each numerical column. I need to remove these outliers and drop the rows that contain them. ...
In the process of developing a program, I am focusing on analyzing smaller companies and gathering data on insider buying. The current script is designed to collect data from every company in a comprehensive table ('http://openinsider.com/latest-penny-stoc ...
I need help regarding importing a table into a pandas dataframe. One of the strings in the table contains the special character 'NF-κB' with the 'kappa' symbol. However, when I use pd.read_table to import the table from 'table_processed.txt', the kappa ch ...
I'm attempting to save a Python Pandas Data Frame as an HTML page. I also want to ensure that the table can be filtered by the value of any column when saved as an HTML table. Do you have any suggestions on how to accomplish this? Ultimately, I want t ...
I am managing a substantial dataframe filled with equipment details, arranged by the equipment name and time sequence. data = [['abc01', 3000.0, 'transac_complete', 'system', '13:10:37', 1], ['abc01' ...
Presented below is a dataset that includes information about a horse's performance: Track FGrating HorseId Last FGrating at Happy Valley Grass Happy Valley grass 97 22609 Happy Valley grass 106 22609 97 Happy Valley grass 104 22609 106 Happy ...
Is there a similar function in the pandas.io.sql library that functions like mysqldb's fetchone? Perhaps something along these lines: qry="select ID from reports.REPORTS_INFO where REPORT_NAME='"+rptDisplayName+"'" psql.read_sql(qry, con=db) reportId = ...
Below is the dataframe provided: padel start_time end_time duration 38 Padel 10 08:00:00 09:00:00 60 40 Padel 10 10:00:00 11:30:00 90 42 Padel 10 10:30:00 12:00:00 90 44 Padel 10 11:00:00 12:30:00 90 46 ...
When it comes to analyzing a trading algo on historical stock market data using Python and pandas, I encountered a problem with looping over large datasets. It's just not efficient when dealing with millions of rows. To address this issue, I started ...
Replacing dozens of strings across multiple columns in thousands of dataframes is currently taking hours due to inefficiency: for df in dfs: for col in columns: for key, value in replacement_strs.items(): df[col] = df[col].str.repla ...
I have been attempting to add a new sheet to an existing excel file while preserving the current content within it. Despite trying various methods, I keep encountering the same error message. Whenever I attempt to write to or save the file, I receive an At ...
I am currently working on a task that involves selecting segments or clauses of sentences based on specific word pairs that these segments should start with. For instance, I'm only interested in sentence segments that begin with phrases like "what does" or ...
Currently, in my pandas program I am working on reading a csv file and converting specific columns into json format. For example, the csv file structure is as follows: id_4 col1 col2 .....................................col100 1 43 56 .......... ...
Within my Dataframe, I have compiled medical records that are structured in this manner: https://i.stack.imgur.com/O2ygW.png The objective is to transform this data into a list of dictionaries resembling the following format: {"parameters" : [{ ...
Having difficulty extracting values from a Json and saving them in a Dataframe. Here is my Json data: { "issues": [ { "expand": "operations", "id": "1", "fields": { ...
Currently, I am utilizing pandas.get_dummies to encode categorical features during the fitting and classification process. Recently, I observed that when using Imputer(), it is inserting averages in the "off" categorical switches that are added in datafram ...
Using Python libraries like requests and BeautifulSoup, I am attempting to scrape the tables from the following Wikipedia page: https://en.wikipedia.org/wiki/Mobile_country_code. While I am able to retrieve all the data within the tables, my goal now is to ...
In the scenario where I have two lists, list1 and list2, along with a single data frame called df1, I am applying filters to append certain from_account values to an empty list p. Some sample values of list1 are: [128195, 101643, 143865, 59455, 108778, 66 ...
I recently inherited a large software project using Python/Flask on the backend and HTML/Javascript on the frontend. I'm now looking to add some interactivity to one of the websites. I have successfully passed a dataframe to the webpage and can display its ...
Currently, I have a functioning function that utilizes a mapping API to return longitude and latitude coordinates based on unstructured address data. When I input an address like "12 & 14 CHIN BEE AVENUE,, SINGAPORE 619937", I receive the output 1.3332439 ...
I am dealing with a pandas Series that contains one numpy array per entry, all of the same length. My goal is to convert this into a 2D numpy array. Despite knowing that Series and DataFrames don't handle containers well, when using np.histogram(.,.)[0] on ...
print(cleaned_train.dtypes) print("--") print(cleaned_test.dtypes) YearOfObservation int64 Insured_Period float64 Residential int64 Building_Painted float64 Building_Fenced float64 Building_Type ...
Although I have come across similar questions to mine multiple times, I have thoroughly reviewed them and still cannot solve my own code. Therefore, I am hoping someone might have the answer. The issue lies in a for loop inside a user-defined function tha ...
I am currently working on a project involving a dataframe where I need to perform certain calculations for each row. The process involves creating lists of 100 numbers in step 1, multiplying these lists together in step 2, and then generating a new datafra ...
It's important to note that this question specifically does not inquire about applying functions on multiple columns during aggregation in pandas. Here is an illustration: Consider the following data frame: A x y foo 0 0 foo 1 1 foo 2 2 foo 3 3 bar 0 ...
I'm new to python and I have a pandas dataframe with multiple columns representing months. I want to compare these columns across a period of x months and flag any rows that have ever had a value of 2 or more. Here is the code snippet I used to generate m ...
My goal is to retrieve the price of a product based on its size, as prices tend to change daily. While I succeeded in extracting data from a website that uses "a class," I am facing difficulties with websites that use div and span classes. Link: Price: $ ...
How can we efficiently convert the following JSON dataset snapshot into a Pandas Data Frame? Importing the file directly results in a format that is not easily manageable. Presently, I am utilizing json_normalize to separate location and sensor into diff ...
I have 3 columns labeled as col1, col2, and col3 with values A, B, or C. The task is to compare the counts of these values in each row and determine which value appears more than once. If there is a tie in the count, the output will be "-" Input: | co ...
I have a DataFrame series that contains sentences, some of which are quite lengthy. Additionally, I possess two dictionaries with words as keys and integers as counts. It's worth noting that not all words from the strings appear in both dictionaries ...
Having some trouble with a Python function... I've got a function that, when I input a date, returns a column with 30 prices (one on each line) and names as the index. [in] getPrice('14/07/2015') [out] apple 10 pear 20 orange 12 banana 23 etc... ...
I am working with a dataset that looks like this df2=df1.head(10) genres imdb_score 0 Action 6.239896 1 Adventure 6.441170 2 Animation 6.576033 3 Biography 7.150171 4 Comedy 6.195246 5 Crime 6.564792 6 Documentary 7.180165 7 Dra ...
Name Class Marks1 Marks2 AA CC 10 AA CC 33 AA CC 21 AA CC 24 I am looking to reformat the data from the original structure shown above to: Name Class Marks1 Marks2 AA CC 10 33 AA CC 21 ...
In the process of developing a function to transmit data to a remote server, I have come across a challenge. My current approach involves utilizing the pandas library to read and convert CSV file data into a dataframe. The next step is to iterate through t ...
I need help transforming comma-delimited strings in a given pandas dataframe into separate rows. For example: COLUMN_1 COLUMN_2 COLUMN_3 "Marvel" "Hulk, Thor, Ironman" "1,7,8" "DC" ...
I have a large excel data file with thousands of rows and columns. Currently, I am using Python and pandas dataframes to analyze this data. My goal is to calculate the annual change for values in column C based on each year for every unique ID found in c ...
I have multiple sets of data arranged in columns within a dataframe, totaling nine lists in all. My objective is to perform matrix operations on every row present across these columns. To illustrate, consider the following operation: O(G) = trace(G*transp ...
I need to split a dataframe into individual files based on unique strings in the "names" column. I have figured out how to do this with a simple function: f = lambda x: x.to_excel(os.getcwd() + '\{}.xlsx'.format(x.name), index=False) df.groupby('names').a ...
I am looking to extract random blocks of data from a dataframe named df. While using df.sample(10) gives me individual samples, it doesn't provide contiguous blocks. Is there a method to sample random blocks (e.g., blocks of 6 consecutive data points) ...
I want to find the count of rows in a DataFrame that occur only once. In this scenario, based on the example provided below, the answer would be 2 as only row indexes 2 and 3 appear once: In [1]: df = pd.DataFrame({'a': [1, 1, 2, 3], 'b': [1, 1, 2, 2]}) ...
After reading this post on reordering indexed rows in a Pandas data frame based on a list, I tried the following code: import pandas as pd df = pd.DataFrame({'name' : ['A', 'Z','C'], 'company' : ['Apple', 'Yahoo','Amazon'], ...
I am working with a sizable DataFrame in pandas that contains approximately 35 million rows, with an average sequence length of about 22: session id servertime 1 3085 2018-10-09 13:20:25.096 1 3671 2018-10-21 08:19:39.0 ...
I'm currently in the process of scraping data from Amazon as part of a project I'm working on. So far, I have set up the following workflow: driver = webdriver.Chrome(executable_path=r"C:\Users\chromedriver.exe") driver.maxim ...
In my project, I encountered a situation where some metrics had missing values for specific years. This led to rows disappearing when creating a pivot table. I wanted to keep these rows in the pivot while preserving any additional columns. However, using t ...
I have a dataset that needs to be cleaned by removing weekend data and after-hours data on weekdays. Once the cleaning is done, I want to use it in a plot without any gaps. It should show as completed and continue seamlessly in the plot. Is there a way to ...
Looking to transform this JSON data into a pandas dataframe. """ { "col": [ { "desc": { "cont": "Asia", "country": "China", ...
I have the subsequent dataset in pandas: X Y 3 7 5 15 4 3 8 11 2 9 I am interested in computing a new column Z which represents the cumulative difference between Y and X, ensuring that Z remains within the bounds ...
There is an abundance of valuable information available on the topic of reading space-delimited data with missing values, but specifically when dealing with fixed-width data. Link to Fixed-Width Files Tutorial Python/Pandas: Reading Space-Delimited File w ...
I'm currently working on structuring my data frame in a specific format: https://i.stack.imgur.com/0W8rC.png Here's what I have attempted so far: labels = ['Rain', 'No Rain'] pd.DataFrame([[27, 63],[7, 268]], columns=labels, index=labels) This is the ...
When working with multiple datafiles, I use the following code to load them: df = pd.concat((pd.read_csv(f[:-4]+'.txt', delimiter='\s+', header=8) for f in files)) The resulting DataFrame looks like this: ...
I'm working with a pandas dataset that has financial data. The first row contains details about which financial KPI is being used. I am looking to split the data into multiple data frames based on the KPI value in the first row. Unnamed: 0 Institution A ...
I have two distinct dataframes, A and B. A = pd.DataFrame({'a'=[1,2,3,4,5], 'b'=[11,22,33,44,55]}) B = pd.DataFrame({'a'=[7,2,3,4,9], 'b'=[123,234,456,789,1122]}) My objective is to merge B with A while excluding any overlapping values in column 'a' betwe ...
Currently, I have an extended version of this coding to iterate through a large dataset and generate new columns: categories = ['All Industries, All firms', 'All Industries, Large firms'] for category in categories: sa[category + ', OP mar ...
Exploring the fantastic code provided by @piRSquared, which can be found below. After adding the condition if row[col2] == 4000, I noticed that it only appears once in the additional column. Consequently, this specific condition causes the function to out ...
I've encountered an issue while training a neural network using keras and tensorflow. Typically, I replace -np.inf and np.inf values with np.nan in order to clean up erroneous data before proceeding with operations such as: Data.replace([np.inf, -np. ...
I have encountered JSON values within my dataframe and am now attempting to iterate through them. Despite several attempts, I have been unsuccessful in converting the dataframe values into a nested dictionary format that would allow for easier iteration. ...
Currently, I am working with a data frame that contains the following information: A B C 1 3 6 My objective is to extract columns A and C and combine them to create column D, which should look like {"A":"1", "C":"6}. Th ...
Being new to Pandas, I am attempting year-over-year comparisons with leap years included. The 'dayofyear' function works well, except when dealing with leap years. Here is the code I have so far: df = pd.read_csv('myfile.csv') df[&apos ...
I currently have a dataframe structured like this: Input df.head(3) groupId Gourpname totalItemslocations 7494732 A {'code': 'DEHAM', 'position': {'lat': 53.551085, 'lon': 9.993682}} 7494733 B {'code': 'DEHAM', 'position': { ...
My DataFrame contains a variety of data types across many columns: col1 int64 col2 int64 col3 category col4 category col5 category Here's an example of one of the columns: Name: col3, dtype: category Categories (8, objec ...
I have a basic dataset containing revenue and cost values. In my particular case, the cost figures can sometimes be negative. My goal is to calculate the ratio of revenue to cost using the following formula: if ((x['cost'] < 0) & (x[&apo ...
I have a dataframe that looks like this: id points 0 1 (2,3) 1 1 (2,4) 2 1 (4,6) 3 5 (6,7) 4 5 (8,9) My goal is to transform it into the following format: id points 0 1 (2,3), (2,4), (4,6) 1 5 (6,7), (8,9) Can anyone pro ...
I have been attempting to export a map that I created using folium in Python to a png file. I came across a post suggesting that this can be achieved by using selenium with the following code snippet: Export a folium map as a png import io from PIL import ...