Category "pandas"

Pandas Merging 101

How can I perform a (INNER| (LEFT|RIGHT|FULL) OUTER) JOIN with pandas? How do I add NaNs for missing rows after a merge? How do I get rid of NaNs after merging?

Comparing lists within dataframe column to another list using numpy's where function

This is my first post at Stackoverflow, so thank you for the help. I am trying to replicate a code where I can match a list within a dataframe to another list,

Read Parquet file form S3 in EMR cluster taling a long time

I am trying to read a parquet file (not compressed) into a pandas dataframe on a EMR cluster. I am using EMR 6.4 and parquet version 1.1.5. We are in the proces

How to handle the variable size json file in python to create DataFrame using pandas

I am trying to build a DataFrame using pandas but I am not able to handle the case when I have the variable size of JSON chunks I am getting. eg: 1st chunk: {'a

Pytest logging ignores dependency warnings

I have a simple python script that leads to a pandas SettingsWithCopyWarning: import logging import pandas as pd def method(): logging.info("info") l

Add a comma after two words in pandas

I have the following texts in a df column: La Palma La Palma Nueva La Palma, Nueva Concepcion El Estor El Estor Nuevo Nuevo Leon San Jose La Paz Colombia Mexico

Generate binary outcome dummy data based on probability of items and its feature

I want to generate a synthetic data from scratch which is a binary outcome sequence data (0/1). My data has following property- For the sake of an example, lets

Pandas to read a excel file from s3 and apply some operation and write the file in same location

i am using pandas to read an excel file from s3 and i will be doing some operation in one of the column and write the new version in same location. Basically ne

How do I calculate the percentage (counted non-numerical values) in Pandas?

Basically, I have the columns date and intensity which I have grouped by date this way: intensity = dataframe_scraped.groupby(["date","intensity"]).count()['sen

Yellowbrick: PredictionError dimensionality issue

I'm trying to use the yellowbrick PredictionError and am running into strange dimensionality issues. I am using yellowbrick version 1.4. Suppose we had this ver

Find last available date if date does not exist in other DataFrame

Suppose that you have two data frames which can be created using code below: df1 = pd.DataFrame(data={'start_date': ['2021-07-02', '2021-07-09',

Unable to identify cause of: ValueError: Must have equal len keys and value when setting with an iterable

Background:I have a script that makes a daily API call for financial data, returns the data as a JSON object, saves it into a pandas df before doing some manipu

Python/Pandas Calculate the mean time (hour) of a Datetime column

I have a Pandas DataFrame (data) with a column ['Date'] in DateTime (date and time) which represents the time of arrival. How to calculate the mean of only the

Plotting the frequency of occurrences per date

I'm new to pandas and plotly. And I have a large csv file with two columns, a date column and a column that contains a string of text (event). Each event is a n

creating a list from a column with multiple lines

I have a Pandas data frame that in one column called SourceDocument I have multiple lines of data in each cell (separated by \n). SourceDocuments PRDS-002039\nP

Can´t copy tupel from one dataframe into another (Length of values does not match length of index)

I want to create columns in a dataframe (df_joined) that contains as values tupels from a second df (df_tupels). The tupels are (10,50) and (20,60). I tried var

I am using pandas to check user input in multiple columns, i want output as entire row which matches input

below is my code: for r in cols: full_row_of_matched = cols[cols.isin([input_ip]).any(axis=1)] exact_column = list(cols.columns[cols.eq(input_ip).any(0)

Find matching name in another table, return value associated w/ column in pandas

I have 2 tables. I want to take DF1 and adjust the values in the tables given the values in DF2. DF2 is simply a groupby of a column in DF1. In domain terms, I

Smart for loop in python for a portfolio performance

this is my first question here, so go easy on me. I've computed a certain portfolio in python, for which I've gotten a dataframe (or list for that matter) of ar

Groupby id and change values for all rows for the earliest date to NaN

I have the following id, i would like to groupby id and then replace value X with NaN. My current df. ID Date X other variables.. 1 1/1/18