I have this series: Initially, I wanted to round to seconds by doing: However, I am trying to merge this dataset to another of higher frequency. So I’d like to round the decimal places of the timestamp instead. Example: 2021-06-15 16:23:04.388 would become 2021-06-15 16:23:04.380 How can I do this? to a timestamp? Answer If .388 should become .380 we
Tag: series
Comparing two sequence columns, and base on a condition add element to Dataframe
Hello, I’m looking for a script in Python that can help me with the following problem: Having the following two columns, I need to create gaps between the sequences in order to make them match: Input Output Index column A column B column A column B 0 1 1 1 1 1 2 2 2 2 2 2 2 2
How do I convert pandas.core.series.Series back to a Dataframe following a groupby?
I tried manipulating a Dataframe and the output was (unexpectedly) of a pandas.core.series.Series type while I was aiming for another Dataframe output. For reference, the original Dataframe looked like this – I was hoping to combine all consecutive rows with the same Character value. So, all ‘Leslie Knope’ lines from “Hello” to “I’m gonna put a lot of fun” would
Copy non-na rows to fill non-na columns using pandas
I have a dataframe like as shown below What I would like to do is copy non-na rows from Test column and paste it in corresponding row under sourcename column When I tried the below, it makes the other rows of sourcename column as NA I expect my output to be like as shown below Answer One idea with Series.fillna:
elegant way to replace multiple list of values with a multiple single value
I have a dataframe like as shown below I would like to replace a list of values like as shown below a) Replace P, PRIMARY,PRI with primary b) Replace S, SECONDARY, SEC with secondary c) Replace T, TERTIARY, THIRD with third I tried the below But is there any other efficient and elegant way to write this in a single
Occurence of a value in many lists
i have a Series Object in pandas with 2 columns, one for the indices and one with lists, I need to find if a value occurs in only one of these lists and return it with the most optimal way. As an example let’s say we have this i need to return 77 because it occurs in only one of
Why the type of pd.DataFrame every items is float, but the dtype of pd.DataFrame is object?
results_table is a pd.DataFrame When I it return Every items is float But when I it returns Why is there such behavior? Answer First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let’s consider an example: As you can see, an object dtype series can hold arbitrary Python
Save and export dtypes information of a python pandas dataframe
I have a pandas DataFrame named df. With df.dtypes I can print on screen: I want to save this information so that I can compare it with other data, type-cast things elsewhere, etc. I want to save it into to a local file, recover it elsewhere in another program where the data can’t go. But I’m not able to figure
Python Pandas iterate over rows and access column names
I am trying to iterate over the rows of a Python Pandas dataframe. Within each row of the dataframe, I am trying to to refer to each value along a row by its column name. Here is what I have: I used this approach to iterate, but it is only giving me part of the solution – after selecting a
How to get the index of ith item in pandas.Series or pandas.DataFrame?
I’m trying to get the index of 6th item in a Series I have. This is how the head looks like: For getting the 6th index name (6th Country after being sorted), I usually use s.head(6) and get the 6th index from there. s.head(6) gives me: and looking at this, I’m getting the index as United Kingdom. So, is there