I want to create a new named column in a Pandas dataframe, insert first value into it, and then add another values to the same column: Something like: How do I do that? Answer Dont do it, because it’s slow: updating an empty frame a-single-row-at-a-time. I have seen this method used WAY too much. It is by far the slowest.
Tag: pandas
How to rename the rows in dataframe using pandas read (Python)?
I want to rename rows in python program (version – spyder 3 – python 3.6) . At this point I have something like that: Before that i wanted to rename my columns: It gave me something like that. But now, I want to rename rows. I want: How can I do it? The main idea is to rename every row
Pandas Multiindex to CSV without duplicate Index
For the life of me I can’t figure out how to remove the duplicate index items created when writing a multiindex dataframe to CSV. While there is this answer out there, it doesn’t apply to me per se because my second level has all different values. This is a chunk of the dataframe I have, it just goes on for
pandas create new column based on row value (condition)
i have a column like this, i need to create a new column based on a condition, if the a[i] and a[i-1] is same, then value is 0 else 1. result should look something like this: The right pandas way to do it? Answer Create boolean mask by sompare for not equal by ne with shifted Series and cast to
Find out the percentage of missing values in each column in the given dataset
input is https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0 and the output should be Answer How about this? I think I actually found something similar on here once before, but I’m not seeing it now… And if you want the missing percentages sorted, follow the above with: As mentioned in the comments, you may also be able to get by with just the first line in
Delete rows with date’s before the required date point based on key value
I have a pd.dataframe that looks like this: So now based on the key_value, I want to drop all the rows that have their date column value before 2018-04-01 I want to have an end output like this: Answer You can just filter your dataframe using Boolean indexing. There is no groupwise operation here. Just remember to convert your series
What is the difference between `assert_frame_equal` and `equals`
I’m curious to find the difference between assert_frame_equal and equal. Both are for checking the equality of two data. It applies for assert_series_equal and assert_index_equal. So what is the difference between equals and testing functions? So far I found was testing functions gives little more flexibility to compare the values, like check_dtpye options etc., and differs from returning values Is
pivot_table requires more memory if dtype is category (MemoryError)
I have the following strange error with pandas(pandas==0.23.1) : I am wondering if this is expected and I am doing something wrong, or if this is a bug in pandas. Should dtype category for str not be very transparent (for this use case)? Answer This is not a bug. What’s happening is pandas.pivot_table is calculating the Cartesian product of grouper
Pandas left join in place
I have a large data frame df and a small data frame df_right with 2 columns a and b. I want to do a simple left join / lookup on a without copying df. I come up with this code but I am not sure how robust it is: I know it certainly fails when there are duplicated keys: pandas
Pandas query function not working with spaces in column names
I have a dataframe with spaces in column names. I am trying to use query method to get the results. It is working fine with ‘c’ column but getting error for ‘a b’ For this I am getting this error: I don’t want to fill up space with other characters like ‘_’ etc. There is one hack using pandasql to