Questions tagged [dataframe]

A 2D tabular structure known as a data frame is commonly used to store data. The rows represent observations, while the columns contain variables that can have different types. This differs from an array or matrix. Various programming languages like R, Apache Spark, deedle, Maple, Python's pandas library, and Julia's DataFrames library refer to this concept as "data frame" or "dataframe." However, MATLAB and SQL choose to use the term "table" to describe the same idea.

What is the best way to replace a segment of a pandas dataframe with another?

Suppose I have two DataFrames, df_a and df_b. I am looking to swap lines 42 through 51 of df_a with the corresponding rows from df_b (same number of rows, but more columns than df_a). The code I am currently using is df_a.loc[45:52,:] = df_b.loc[45:52," ...

pythonpandasdataframe

Learning to extract information from space-delimited data with varying row types and numerous missing values

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 ...

pythonpandasdataframe

convert all the characters to lowercase in JSON

I have a JSON file containing information about classes and annotations like this- {"classes":["BUSINESS","PLACE","HOLD","ROAD","SUB","SUPER","EA","DIS","SUB&quo ...

pythonpandasdataframedictionarylowercase

Converting serial data into columns within a pandas dataframe through row transformation

I'm looking to convert individual rows in a dataframe into columns with their corresponding values. My pandas dataframe has the following structure (imported from a json file): Key Value 0 _id 1 1 type house 2 surface ...

pythonpandasdataframepivottranspose

Creating a dataframe with fixed intervals - Python

Within my dataset, I have a dataframe that includes the following columns (only showing a portion): START END FREQ VARIABLE '2017-03-26 16:55:00' '2017-10-28 16:55:00' 1234567 x &ap ...

pythonpandasperformancenumpydataframe

Is there a way to calculate the percentile of a column in a dataframe by only taking into account the values that came before

I have a dataset containing numerical values in a column and I want to calculate the percentile of each value based on only the preceding rows in that same column. Here's an example: +-------+ | col_1 | +-------+ | 5 | +-------+ | 4 | +-------+ | ...

pythonpandasdataframepercentile

Create a new boolean column by comparing values in two different columns from separate DataFrames

I'm just starting with pandas and I have a task where I need to compare two dataframes based on two columns from each DataFrame. The first DataFrame, df1, has columns for Brand and Signal_range. The second DataFrame, df2, contains columns for order, Brand, ...

pythonpandasdataframe

The chunk size is not initiating from the initial row in the CSV file

I am working with a large CSV file in Python 3 that I need to split and save into two separate files. Using the chunksize parameter, I specify how many rows should be included in each file. The first code is designed to read the specified number of row ...

pythonpython-3.xpandascsvdataframe

What is the procedure for swapping out a value/phrase in a column within a data frame?

Looking to update specific strings in a column within a data frame, which currently appears as: df["column"] ------------------ 1. Ne Road 2. Rosemarys street se 3. Plunkett pkwy 4. and so on..... There are thousands of values like these that nee ...

pythonexcelstringpandasdataframe

Generate sections to tally the different genres present in films

I am seeking guidance on modifying my Python code to correctly determine the gender columns. Below is the code I currently have, along with the output it produces and the desired output: import numpy as np import pandas as pd df=pd.read_csv("titles.cs ...

pythonpandasdataframe

Generate a dataframe by combining several arrays through an iterative process using either a for loop or a nested loop in

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 ...

pythonpandasnumpydataframefor-loop

Converting a flattened column of type series or list to a dataframe

I am looking to extract specific data from my initial dataset which is structured as shown below: time info bis as go 01:20 {'direction': 'north', abc {'a':12,'b':20 } yes ...

pythonpandaslistdataframe

Combining three PySpark columns into a single struct

Hello, I am a newcomer to PySpark and currently grappling with a challenge that needs solving. I have the task of merging three columns based on the values in a fourth column: Let's consider an example table layout like this: store car color cyli ...

pythondataframepysparkapache-spark-sql

Finding corresponding elements between a list and dataframe in Python

I am currently working with some lists as shown below: l1 = ['Category=worker,manager','Name=Ana,Tom', 'Task=Cleaning,Plumbing'] In addition to these lists, I also have a dataframe called df: Name | Category | Task | OrderNum Bryan | ...

pythonregexpandaslistdataframe

What is the best way to iterate through a series of dataframes and perform operations using a for loop?

I've encountered a common issue and I'll use the Titanic dataset to illustrate. In order to perform operations on both the train and test sets simultaneously, I merged them together: combined = [train_df, test_df] In addition, I streamlined the titles fo ...

pythonloopsdataframefor-loopconcatenation

Looking to iterate through a dataframe and transform each row into a JSON object?

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 ...

pythonjsonpandasdataframe

How can JSON data that is disorganized and undefined be properly transformed into a DataFrame?

Trying to figure out how to convert improperly parsed JSON data into a Pandas DataFrame has been quite the challenge. Utilizing Python (3.7.1), I attempted to read the JSON data in the usual way. While my code seems to work if I utilize transpose or axis= ...

pythonjsonpandasdataframejupyter-notebook

Updating missing values in a DataFrame row by replacing them with values from different rows that match a specific column value

I currently have a DataFrame that includes a column with non-unique values (in this instance, addresses) along with other columns containing related information. df = pd.DataFrame({'address': {0:'11 Star Street', 1:'22 Milky Way&ap ...

pythonpandasnumpydataframe

Performing mathematical operations with Pandas on specific columns based on conditions set by other columns in the dataset

I am working with a Pandas DataFrame import pandas as pd inp = [{'c1':1, 'c2':100}, {'c1':1,'c2':110}, {'c1':1,'c2':120},{'c1':2, 'c2':130}, {'c1':2,'c2':14 ...

pythonpandasdataframe

What is the best way to compress or combine a pandas dataframe vertically?

My dataset consists of a pandas dataframe with multiple columns, but for now let's only examine two: df = pd.DataFrame([['hey how are you', 'fine thanks',1], ['good to know', 'yes, and you',2], ['I am fine','ok',3] ...

pythonpandasdataframe

Steps to create a histogram using a dataframe

I've been working on plotting data from various age groups in the form of a histogram. I have properly binned the age groups and when I visualize the data as a bar or line graph, everything appears to be fine. However, when I attempt to create a histogram, ...

pythonpandasdataframematplotlibhistogram

Utilize designated columns to create a new column by associating it with JSON data

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 ...

pythonjsonpandasdataframe

Pandas encountered a ValueError while attempting to add a new column, as it cannot reindex from a duplicate axis

Here is the information I have: Inv Dt Due Dt 22 2020-10-31 2020-11-15 181 2020-10-01 2020-11-15 182 2020-10-01 2020-11-15 1845 2020-10-30 2020-11-14 2185 2020-10-14 2020-10-16 ... ... ... 308085 ...

pythondataframe

Ways to merge two dataframes of varying lengths when both have datetime indexes

I am dealing with two different dataframes as shown below: a = pd.DataFrame( { 'Date': ['01-01-1990', '01-01-1991', '01-01-1993'], 'A': [1,2,3] } ) a = a.set_index('Date') ------------- ...

pythonpandasdataframeconcatenation

Calculate the sum of each column in a pandas dataframe using user-defined functions in Python

Showing my dataset: df = [{'id': 1, 'name': 'bob', 'apple': 45, 'grape': 10, 'rate':0}, {'id': 1, 'name': 'bob', 'apple': 45, 'grape': 20, ...

pythonpandasdataframeaggregate

Tips for implementing an IF statement within a FOR loop to iterate through a DataFrame efficiently in Python

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 ...

pythonpandasfor-loopif-statementdataframe

What is the method to add a value based on two specific cells in a row of a Dataframe?

Here is the Dataframe layout I am working with: Dataframe layout This is the code snippet I have written: if (df_.loc[(df_['Camera'] == camera1) & (df_['Return'].isnull())]): df_.loc[(df_['Camera'] == camera1) & (df_['Return'].isnull()), 'Retu ...

pythondataframeinsertrowcell

Navigating through monthly and yearly data in an extensive Python Pandas dataframe

Currently, I am developing a program to analyze 324 NetCDF files containing latitudes, longitudes, times, and sea surface temperature (SST) values from January 1994 to December 2020. The objective is to determine the average monthly SST within a specified ...

pythonpandasdataframenetcdf

Examining data within individual groups in a Python dataframe and making comparisons

I have a dataset that has the following structure - id amount date category code a201 100 12-10-2022 a a201 a101 70 12-10-2022 a a201 a102 90 12-10-2022 a a201 b24 150 12-10-2022 b b24 b13 120 12-10-2022 b b24 c71 10 12-10-2022 c c71 c1 ...

pythonpandasdataframe

Counting and labeling cumulative totals with Pandas

My pandas dataframe has the following structure: +---------+---------+------------+--------+ | Cluster | Country | Publishers | Assets | +---------+---------+------------+--------+ | South | IT | SS | Asset1 | | South | IT | SS ...

pythonpandasdataframe

Combine, or blend two pandas dataframes into one

I have a dilemma with merging two dataframes. The first dataframe has a shape of (10840, 109) while the second one is empty with a shape of (0,112). My attempt to merge them using df_part_2 = pd.concat([df_revisi_data,df_migrasi_part2],axis=1) resulted in ...

pythonpandasdataframemergeconcatenation

Transforming Dictionary Data in JSON DataFrame into Individual Columns Using PySpark

I have a JSON file with a dictionary that resembles the following: "object1":{"status1":388.646233,"status2":118.580561,"status3":263.673222,"status4":456.432483} I want to extract status1, status2, status ...

pythonsqldataframepysparkpalantir-foundry

Manipulate DataFrame in Python using masks to generate a fresh DataFrame

I have a DataFrame that includes columns for date, price, MA1, MA2, and MA3. After filtering the data based on a specific condition, I get a subset of rows where MA1, MA2, and MA3 are equal. date price MA1 MA2 MA3 date1 price1 11 11 11 date4 pri ...

pythonpandasdataframenumpy

Generating a dataframe with cells that have a combination of regular and italicized text using the sjPlot package functions

I've been trying to set up a basic data table where the genus name in the "Coral_taxon" column is italicized, but the "spp." part following it remains lowercase. I thought of using the expression() function for each row in "Coral_taxon," but so far, I have ...

htmlrdataframesjplotitalic

Is there a way to fetch values from a dataframe one day later, which are located one row below the largest values in a row of another dataframe of identical shape?

I am working with two data frames that share the same date index and column names. My objective is to identify the n largest values in each row of one dataframe, then cross-reference those values in the other dataframe one day later. The context here is f ...

pythonpandasdataframe

Analyzing data distribution using Python

Let's say I have a set of data as follows: Length Width Height 100 140 100 120 150 110 140 160 120 160 170 130 170 190 140 200 200 150 210 210 160 220 ...

pythonpandasdataframematplotlibdata-analysis

Saving a Python pandas dataframe to a CSV file with line breaks represented as text

How can I write the sentence "hello world ." in a cell without " " being interpreted as end of line, so that when opened in a text editor it appears exactly as "hello world ."? ...

pythonpandasdataframecsvexport-to-csv

Is there a way for me to generate a tally of the frequency of each number's occurrence in column B?

If I have a dataset like this: A B 1 1401 2 1401 3 1401 4 1601 5 2201 6 2201 7 6401 8 6401 9 6401 10 6401 I want to achieve the following output: L1 = [1401, 1601, 2201, 6401] L2 = [3, 1, 2, 4] (representing how ...

pythonpandasdataframe

transform a JSON file containing multiple keys into a single pandas DataFrame

I am trying to convert a JSON file into a DataFrame using json_normalize("key_name"). I have successfully converted one key into a DataFrame, but now I need to convert all keys into one single DataFrame. { "dlg-00a7a82b": [ { "ut ...

pythonjsonpandasdataframedata-analysis

Guide to establishing a connection to CloudantDB through spark-scala and extracting JSON documents as a dataframe

I have been attempting to establish a connection with Cloudant using Spark and read the JSON documents as a dataframe. However, I am encountering difficulties in setting up the connection. I've tested the code below but it seems like the connection p ...

jsonscaladataframeapache-sparkcloudant

The function Dataframe.reset_index() fails to operate properly following a concat operation

While researching, I came across two other related questions that didn't provide the solution I needed: [1], [2]. The problem arose when I concatenated several columns of df at the beginning and end of df_new. This operation led to an increase in indexin ...

pythonpandasdataframeindexing

Decrease the index's level

After running a pivot table, I have the result below indicating the customer grades that visited my stores. Using the 'droplevel' method, I managed to flatten the column header into one layer. Now, I am looking to do the same for the index - removing 'Grad ...

pythonpandasdataframemultiple-columnsflatten

When attempting to sort, the rows in my pandas dataframe suddenly become rearranged

My dataframe has a header that looks like this: Out[8]: Date Value 0 2016-06-30 481100.0 1 2016-05-31 493800.0 2 2015-12-31 514000.0 3 2015-10-31 510700.0 I am looking to set the Dates column as the index and then sort the rows base ...

pythonpython-3.xpandasdataframe

I am having trouble assigning a value to a specific position in a dataframe

I've been attempting to set a nested dictionary at a specific position, but it just won't work. Here's the code snippet I have: def history_current(df): df_this = df.copy() leid_val = {} leid_index = {} run_seq_min = min(df.run_seq.values) ...

pythonpandasnumpydataframe

What is the best way to rearrange a lengthy string of dates and timestamps that have been combined with commas using Python?

I have a column named 'datetimes' that stores multiple dates along with timestamps as strings. I need to extract the earliest and latest dates excluding the timestamps into new columns 'earliest_date' and 'last date'. The challenge lies in the fact that t ...

pythonpandasdataframepython-datetime

What is the best way to extract all labels from a column that has been one hot encoded?

Converting One Hot Encoded Columns to Multi-labeled Data Representation. I am looking to transform over 20 one hot encoded columns into a single column with label names, while also considering the fact that the data is multi-labeled. I aim for the label co ...

pythondataframenlpdataset

Creating a new column in a Pandas dataframe that contains a list of values based on the repetition of rows in another

I am currently dealing with a dataframe that looks like the following: ID Cluster Product 1 4 'b' 1 4 'f' 1 4 'w' 2 7 'u' 2 7 'b' 3 ...

pythonpandasdataframegroup-bypandas-groupby

Tips for organizing a dataframe with numerous NaN values and combining all rows that do not begin with NaN

Below is a df that I have: df = pd.DataFrame({ 'col1': [1, np.nan, np.nan, np.nan, 1, np.nan, np.nan, np.nan], 'col2': [np.nan, 2, np.nan, np.nan, np.nan, 2, np.nan, np.nan], 'col3': [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 3, np.nan], ' ...

pythonpandasdataframe

What is the best method for sorting through data entries using only designated time parameters?

I have collected data that looks like this: Out[504]:df time1 temp1 temp2 dcity1 dcity2 s 0 00:20:00 7 7 1 1 1.000000 1 00:20:00 7 7 1 1 1.000000 2 00 ...

pythonpandasdataframe

A Fresh Approach to Altering Dictionary Organization

In Python, I am working with an object type that contains multiple entries in the data-object. An example entry is shown below: > G1 \ jobname x [3. ...

pythonpandasdataframeparsingdata-structures

Displaying dataframes with Pandas

While attempting to follow the "Machine Learning" tutorial by Microsoft, I encountered an issue during the practical part. I simply copied the code and tried running it from the Linux terminal, but unfortunately, nothing was returned. The video demonstrati ...

pythonpandasdataframe

What is the best way to transform a series of probabilities into binary values of 0 and 1?

Given a dataset with two columns 'y' and 'proba', where 'y' contains class labels '0' and '1' and 'proba' represents the probability. The task is to create a list called 'y_hat' based o ...

pythonpython-3.xpandasdataframe

struggling to open a csv file using pandas

Embarking on my journey into the world of data mining, I am faced with the task of calculating the correlation between 16 variables within a dataset consisting of around 500 rows. Utilizing pandas for this operation has proven to be challenging as I encoun ...

pythonpython-3.xpandasdataframe

Combining DataFrames in Pandas with custom weights

This question resembles: Merge DataFrames in Pandas using the mean, however, I require arbitrary weights instead of just the simple mean. I possess two DFs structured like this: df1 from_code to_code frequency a a 0.2 a b 0.4 df2 from_code ...

pythonpandasdataframe

Converting JSON arrays into structured arrays using Spark

Does anyone know how to convert an Array of JSON strings into an Array of structures? Sample data: { "col1": "col1Value", "col2":[ "{\"SubCol1\":\"ABCD\",\"SubCol ...

jsonscaladataframeapache-sparkapache-spark-sql

Steps for creating a new dataset while excluding specific columns

Is it possible to achieve the task of extracting a new dataframe from an existing one, where columns containing the term 'job', any columns with the word 'birth', and specific columns like name, userID, lgID are excluded? If so, what would be the most eff ...

pythonpandasnumpydataframeindexing

Combine pandas rows by their values and missing data cells

Here is a sample of my dataframe: ID VALUE1 VALUE2 VALUE3 1 NaN [ab,c] Good 1 google [ab,c] Good 2 NaN [ab,c1] NaN 2 First [ab,c1] Good1 2 First [ab,c1] 3 NaN [ab,c] Good The requirement is as follows: Each row with t ...

pythonpandasdataframe

Utilizing re.sub for modifying a pandas dataframe column while incorporating predefined restrictions - **Highly Beneficial**

This particular problem took some time for me to solve, as there were only bits and pieces of information on stack overflow. I wanted to share my solution in case anyone else is facing the same issue. Objective: 1- Modify strings in a entire pandas DataF ...

pythonregexpandasdataframereplace

Generating a new column by applying a condition to existing column values

In my dataset, there is a column labeled "brand" with different values: brand Brand1 Brand2 Brand3 data.brand = data.brand.astype(str) data.brand = data.brand.replace(r'^\s*$', np.nan, regex=True) data['branded'] ...

pythonpandasdataframenumpy

Transform a folding process into a vectorized operation within a dataset

If we consider a sample dataframe as shown below: df = pd.DataFrame({'A': [np.nan, 0.5, 0.5, 0.5, 0.5], 'B': [np.nan, 3, 4, 1, 2], 'C': [10, np.nan, np.nan, np.nan, np.nan]}) >>> df A B C 0 NaN ...

pythonpandasdataframenumpyvectorization

Improving the efficiency of cosine similarity calculations among rows in a dataframe

I have a pandas DataFrame that contains a large collection of data (~150k rows), structured with two columns: Id and Features. Each row in the Features column is a 50-position numpy array. My objective is to select a random feature vector from the dataset ...

pythonpandasdataframenumpy

A guide on setting custom boundaries for the age column in a Python dataframe

My goal is to define an upper bound and lower bound based on user input, where the upper bound is the user's input plus 10. Create a DataFrame df = pd.DataFrame({ 'VIN':['v1', 'v1', 'v1', 'v1', 'v1', 'v2', 'v2', 'v2', 'v2', 'v2'], 'Revenue':[30, 50 ...

pythonpython-3.xpandaslistdataframe

What is the best way to sort a loop and keep the data for future use?

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 ...

pythonpandasdataframeseleniumweb-scraping

Compare three columns in Pandas and display the outcome if the count exceeds one

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 ...

pythonpandasdataframe

What is causing the dtype to be "object" even though all columns consist of float64 and int64 data

print(cleaned_train.dtypes) print("--") print(cleaned_test.dtypes) YearOfObservation int64 Insured_Period float64 Residential int64 Building_Painted float64 Building_Fenced float64 Building_Type ...

pythonpandasdataframeobject

Showing the names of the columns in a Pandas dataframe for individual rows based on a specific condition

Currently, I am working on a project involving Python Pandas Dataframe. My main goal is to display a list of columns for each row in the dataset. It's important to note that each column can only have a value of either 0 or 1. Here's an example: id A B ...

pythonpandasdataframe

Issue with unnamed column in Pandas dataframe prevents insertion into MySQL from JSON data

Currently, I am working with a large JSON file and attempting to dynamically push its data into a MySQL database. Due to the size of the JSON file, I am parsing it line by line in Python using the yield function, converting each line into small pandas Data ...

pythonmysqljsonpandasdataframe

Substitute values in a dataframe using specific index positions from a separate list

I am currently working with a dataframe that contains a column for dates. My goal is to replace the values in this column based on a specific list of indexes. For example, I have a list called wrong_dates_indexes which contains the indexes where the date i ...

pythonpython-3.xpandasdataframedata-science

The Pandas DataFrame is displaying cells as strings, but encountered an error when attempting to split the cells

I am encountering an issue with a Pandas DataFrame df. There is a column df['auc_all'] that contains tuples with two values (e.g. (0.54, 0.044)) Initially, when I check the type using: type(df['auc_all'][0]) >>> str However, when I attempt to co ...

pythonpandasdataframesplittuples

What is the most efficient way to iterate through a list of URLs containing JSON data, transform each one into a dataframe, and then store them in individual CSV files?

My goal is to fetch data from various URLs, transform each JSON dataset into a dataframe, and store the resulting data in tabular form such as CSV. I am currently experimenting with this code snippet. import requests url_list = ['https://www.chsli.or ...

pythonjsonpython-3.xdataframe

Combine dataframes while excluding any overlapping values from the second dataframe

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 ...

pythonpandasdataframe

How can I calculate the difference between the values in two columns in pandas using python and store the results in a new column?

Recently, I was experimenting with Pandas operations and delved into conditional operations. To provide some context, I have two dataframes structured as follows: Dataframe 1 (df_1): Time Coupons_Sold First_Quarter-2021 1041 Second_Quarter-2021 ...

pythonpandasdataframe

Combine a column in pandas when all rows are identical

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 ...

pythonpandasdataframepandas-groupby

Creating a Pandas DataFrame from Scraped Code with bs4/selenium in Python: A Step-by-Step Guide

I am currently working on converting two variables from a parsed table into a Pandas Dataframe for printing to Excel. Just a heads up: I had previously asked a similar question, but it wasn't addressed thoroughly. I specifically needed guidance on creatin ...

pythonpandasseleniumdataframeparsing

Guide to obtaining the total number of a category depending on a different category and visualizing the outcome

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, ...

pythonpandasdataframematplotlib