I have a Dataframe containing a single column with a list of file names. I want to find all rows in the Dataframe that their value has a prefix from a set of know prefixes.
I know I can run a simple for loop, but I want to run in a Dataframe to check speeds and run benchmarks - it's also a nice exercise.
What I had in mind is combining str.slice with str.index but I can't get it to work. This is what I have in mind:
import pandas as pd
file_prefixes = {...}
file_df = pd.Dataframe(list_of_file_names)
file_df.loc[file_df.file.str.slice(start=0, stop=upload_df.file.str.index('/')-1).isin(file_prefixes), :] # this doesn't work as the index returns a dataframe
My hope is that said code will return all rows that the value there starts with a file prefix from the list above.
In summary, I would like help with 2 things:
Combining slice and index
Thoughts about better ways to achieve this
Thanks
I will use startswith
file_df.loc[file_df.file.str.startswith(tuple(file_prefixes)), :]
Related
My derived column is a substring of another column but the new string must be extracted at varying positions. In the code below I have done this using a lambda. However, this is slow. Is it possible to achieve the correct result using str.slice or is there another fast method?
import pandas as pd
df = pd.DataFrame ( {'st_col1':['aa-b', 'aaa-b']} )
df['index_dash'] = df['st_col1'].str.find ('-')
# gives wrong answer at index 1
df['res_wrong'] = df['st_col1'].str.slice (3)
# what I want to do :
df['res_cant_do'] = df['st_col1'].str.slice ( df['index_dash'] )
# slow solution
# naively invoking the built in python string slicing ... aStr[ start: ]
# ... accessing two columns from every row in turn
df['slow_sol'] = df.apply (lambda x: x['st_col1'] [ 1+ x['index_dash']:], axis=1 )
So can this be sped up ideally using str.slice or via another method?
From what I understand you want to get the last value after the "-" in st_col1 and pass that to a single column for that just use split
df['slow_sol'] = df['st_col1'].str.split('-').str[-1]
No need to identify the index, and them slicing it again on the given index dash. This will surely be more efficient then what you are doing, and cut a lot of steps
I read in the excel file like so:
data = sc.read_excel('/Users/user/Desktop/CSVB.xlsx',sheet= 'Sheet1', dtype= object)
There are 3 columns in this data set that I need to work with as .obs but it looks like everything is in the .X data matrix.
Anyone successfully subset after reading in the file or is there something I need to do beforehand?
Okay, so assuming sc stands for scanpy package, the read_excel just takes the first row as .var and the first column as .obs of the AnnData object.
The data returned by read_excel can be tweaked a bit to get what you want.
Let's say the index of the three columns you want in the .obs are stored in idx variable.
idx = [1,2,4]
Now, .obs is just a Pandas DataFrame, and data.X is just a Numpy matrix (see here). Thus, the job is simple.
# assign some names to the new columns
new_col_names = ['C1', 'C2', 'C3']
# add the columns to data.obs
data.obs[new_col_names] = data.X[:,idx]
If you may wish to remove the idx columns from data.X, I suggest making a new AnnData object for this.
I am conducting an analysis of a dataset. To find my results, I use this line of code:
new_df = df_ncis.groupby(['state', 'year'])['totals'].mean()
The object returned by this statement is a Series, when it should be a dataframe. I don't understand why this happened, or how to solve this issue. Also, one of the columns of the new object is missing its name. Here is the github link for the project: https://github.com/louishrm/gundataUS.
Any help would be great.
You are filtering the result by ['totals'] which is a series.
try this instead
new_df = df_ncis[['state', 'year', 'totals']].groupby(['state', 'year']).mean()
which will give you a dataframe with your 3 columns.
or if you want it as a dataframe of one column (Note the double brackets)
new_df = df_ncis.groupby(['state', 'year'])[['totals']].mean()
I am trying to port some code from Pandas to Koalas to take advantage of Spark's distributed processing. I am taking a dataframe and grouping it on A and B and then applying a series of functions to populate the columns of the new dataframe. Here is the code that I was using in Pandas:
new = old.groupby(['A', 'B']) \
.apply(lambda x: pd.Series({
'v1': x['v1'].sum(),
'v2': x['v2'].sum(),
'v3': (x['v1'].sum() / x['v2'].sum()),
'v4': x['v4'].min()
})
)
I believe that it is working well and the resulting dataframe appears to be correct value-wise.
I just have a few questions:
Does this error mean that my method will be deprecated in the future?
/databricks/spark/python/pyspark/sql/pandas/group_ops.py:76: UserWarning: It is preferred to use 'applyInPandas' over this API. This API will be deprecated in the future releases. See SPARK-28264 for more details.
How can I rename the group-by columns to 'A' and 'B' instead of "__groupkey_0__ __groupkey_1__"?
As you noticed, I had to call pd.Series -- is there a way to do this in Koalas? Calling ks.Series gives me the following error that I am unsure how to implement:
PandasNotImplementedError: The method `pd.Series.__iter__()` is not implemented. If you want to collect your data as an NumPy array, use 'to_numpy()' instead.
Thanks for any help that you can provide!
I'm not sure about the error. I am using koalas==1.2.0 and pandas==1.0.5 and I don't get the error so I wouldn't worry about it
The groupby columns are already called A and B when I run the code. This again may have been a bug which has since been patched.
For this you have 3 options:
Keep utilising pd.Series. As long as your original Dataframe is a koalas Dataframe, your output will also be a koalas Dataframe (with the pd.Series automatically converted to ks.Series)
Keep the function and the data exactly the same and just convert the final dataframe to koalas using the from_pandas function
Do the whole thing in koalas. This is slightly more tricky because you are computing an aggregate column based on two GroupBy columns and koalas doesn't support lambda functions as a valid aggregation. One way we can get around this is by computing the other aggregations together and adding the multi-column aggregation afterwards:
import databricks.koalas as ks
ks.set_option('compute.ops_on_diff_frames', True)
# Dummy data
old = ks.DataFrame({"A":[1,2,3,1,2,3], "B":[1,2,3,3,2,3], "v1":[10,20,30,40,50,60], "v2":[4,5,6,7,8,9], "v4":[0,0,1,1,2,2]})
new = old.groupby(['A', 'B']).agg({'v1':'sum', 'v2':'sum', 'v4': 'min'})
new['v3'] = old.groupby(['A', 'B']).apply(lambda x: x['v1'].sum() / x['v2'].sum())
I am new to Pyspark and am a bit confused on how to think of the problem.
I have a large dataframe and I would like to filter down every subset of that dataframe based on two columns and run it through the same algorithm.
Here is an example of how I run it (extremely inefficiently) now:
for letter in ['a', 'b', 'c']:
for number in [1, 2, 3]
filtered_DF_1, filtered_DF_2 = filter_func(DF_1, DF_2, letter, number)
process_function(filtered_DF_1, filtered_DF_2)
Basic filter function:
def filter_func(DF_1, DF_2, letter, number):
DF_1 = DF_1.filter(
(F.col("Letter") == letter) &
(F.col('Number') == number)
)
DF_2 = DF_2.filter(
(F.col("Letter") == letter) &
(F.col('Number') == number)
)
return DF_1, DF_2
Since this is Pyspark, I would like to parallelize it, since each iteration of the function can run independently.
Do I need to do some sort of mapping to get all my data subsets?
And then do I need to do anything to the process_function to make it available to all nodes as well to run and return an answer?
What is the best way to do this?
EDIT:
The process_function takes the filtered dataset and runs it through about 7 different functions that are already written in 300 lines of pyspark --> the end goal is to return a list of timestamps that are overbooked based on a bunch of complicated logic.
I think my plan is to build a dictionary of letter --> [number], then explode that list to get every permutation and create a dataset from that. Then map through that, and hopefully am able to create a udf for my process_function.
I don't think you need to worry a lot about parallelizing or the execution plan because the spark catalyst does it in the background for you. Also better to avoid UDF, you can do it mostly with inbulit function.
Are you doing a transformation function or an aggregate function inside you process_func?
Please provide any test data and suitable example of expected output. That would help in better answering..