I'm using the function del df['column name'] to delete the column in Pandas but there is the error as the attached picture. I have no idea why it does not work. Much appreciated for any help to solve the problem.
You should use the drop method instead.
df.drop(columns='column_name')
And if you want to chage the original Dataframe you should add the inplace=True as an argument to the method.
Also, avoid posting pictures if possible. Posting the written code is often more usufel and makes it easier for someone to help you!
Related
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I am facing problem in converting DataFrame A to DataFrame B. I have tried using the .transpose() method. However, it did not work. Please help if you can. I cannot share the code, as it is confidential.
Please check these functions:
melt
explode
stack
Also, converting your df (or parts of it) to dictionary may be useful: to_dict.
I am trying to format a pandas DataFrame value representation.
Basically, all I want is to get the "Thousand" separator on my values.
I managed to do it using the pd.style.format function. It does the job, but also "breaks" all my table original design.
here is an example of what is going on:
Is there anything I can do to avoid doing it? I want to keep the original table format, only changing the format of the value.
PS: Don't know if it makes any difference, but I am using Google Colab.
In case anyone is having the same problem as I was using Colab, I have found a solution:
.set_table_attributes('class="dataframe"') seems to solve the problem
More infos can be found here: https://github.com/googlecolab/colabtools/issues/1687
For this case you could do:
pdf.assign(a=pdf['a'].map("{:,.0f}".format))
I am trying to calculate the mean of different columns using groupby.
Here is my code.
However, as soon as I try to calculate mean, the error 'no numeric types to aggregate' appears. What is wrong with my code? Please help me!!! Thank you so much.
can you please post your code as text and some example data?
What are the contents of data['low_stress'] and data['high_stress']?
My guess is, you use pd.Series([low_stress]) and thereby instantiate a series of an array of your data. Using pd.Series(low_stress) will probably fix your problem.
Here are my data and index value image :
As in the snap pandas Dataframe returning two values. What could be possibly wrong? I am beginner, sorry for the bad editing.
I think I see the issue.
data['Title'].iloc[0]
Try something like this. I think the .head() portion of the code is causinng you issues
I'm relatively new to PySpark. I understand that unlike pandas, PySpark dataframes are not mutable and does not allow tranformation in-place as described in this. So, I'm curious to know if can store the mutated dataframe as same name of the old one like,
joindf = joindf.withColumn("label", joindf["show"].cast("double"))
I know that this operation is perfectly alright with other programming languages, overwriting the old value. Just want to confirm if it is same for PySpark too. Any help is appreciated. Thanks in advance.