My data frame is survey data that I have got from a .csv file. One of the columns is age and I am looking to remove all respondents under 18 years of age. I'll then need to isolate age groups (18-24, 25-35, etc) into their own dataframes that I can do frequency distributions for.
The R code is simple enough:
x.sub <- subset(x.df, y > 2)
But I can't figure out how to use the r() function to get my dataframe variable from python into an R statement. It feels as though there ought to be a .subset() function in the rpy2 DataFrame class. But if it exists, I can't find it.
Using rpy2 2.2.0-dev (should be the same with 2.1.x)
from rpy2.robjects.vectors import DataFrame
dataf = DataFrame.from_csvfile("my/file.csv")
dataf_subset = dataf.rx(dataf.rx2("age").ro >= 18, True)
That one exact example is not in the documentation (and may be should be there), but it's constituting elements are:extracting elements and R operators on vectors
Related
I am currently working on pandas structure in Python. I wrote a function that extracts data from Pandas data frame and stores it in lists. The code is working but I feel like there is a part that I could write in one for loop instead four for loops. I will give you an example below. The idea of this part of the code is to extract four columns from a pandas data frame into four lists. I did it with 4 separate for loops but I want to have one loop that does the thing.
col1,col1,col1,col1 = [],[],[],[]
for j in abc['col1']:
col1.append(j)
for k in abc['col2']:
col2.append(k)
for l in abc['col3']:
col3.append(l)
for n in abc['col4']:
col4.append(n)
And my idea is to write a one for loop that does all the code. I tried to do something like this, but it doesn't work.
col1,col1,col1,col1 = [],[],[],[]
for j,k,l,n in abc[['col1','col2','col3','col4']]
col1.append(j)
col2.append(k)
col3.append(l)
col4.append(n)
Can you help me with this idea to wrap four for loops into the one? I would appreciate your help!
You don't need to use loops at all; you can just convert each column into a list directly.
list_1 = df["col"]to_list()
Have a look at this previous question.
Treating a panda dataframe like a list usually works, but is very bad for performance. I'd consider using the iterrows() function instead.
This would work as in the following example:
col1,col2,col3,col4 = [],[],[],[]
for index, row in df.iterrows():
col1.append(row['col1'])
col2.append(row['col2'])
col3.append(row['col3'])
col4.append(row['col4'])
It's probably easier to use pandas.values and then numpy.ndarray.to_list():
col = ['col1','col2','col3']
data = []*len(col)
for i in range(len(col)):
data[i] = df[col(i)].values.to_list()
I currently trying to extract several dataframes from a dictionary. The problem is, that the number of dataframes will vary, sometimes I'll have two dataframes in there and sometimes 30.
At the beginning I create a dictionary (dict_of_exceptions) from a dataframe (exceptions_df). In this dictionary I'll have several dataframes depending on how many different 'Source Wells' I have. With the current code I can extract the first dataframe from the dictionary which is j:
dict_of_exceptions = {k: v for k, v in exceptions_df.groupby('Source Well') }
print (dict_of_exceptions)
for k in dict_of_exceptions.keys():
j = dict_of_exceptions[k]
Could someone help me modify the last line to go trough the dictionary and extract each dataframe (and name them like the corresponding key)?
I think I get your intention, but could not really read your intentions from your code though. Currently, as #cyrilb38 stated in comments, your loop is overriding j, so you would only be able to see the result of last iteration. Anyways, rather transforming use dataframe instead, and I think (may be wrong) that you call your row a dataframe. Replacing a groupby object with dict is not what you wanted, or it is just prolonging the process for nothing.
If you want to see the info of Well X only for example, try this
exceptions_df[exceptins_df['Source Well'] == 'Well X']
I have a DataFrame with a column named 'UserNbr' and a column named 'Spclty', which is composed of elements like this:
[['104', '2010-01-31'], ['215', '2014-11-21'], ['352', '2016-07-13']]
where there can be 0 or more elements in the list.
Some UserNbr keys appear in multiple rows, and I wish to collapse each such group into a single row such that 'Spclty' contains all the unique dicts like those in the list shown above.
To save overhead on appending to a DataFrame, I'm appending each output row to a list, instead to the DataFrame.
My code is working, but it's taking hours to run on 0.7M rows of input. (Actually, I've never been able to keep my laptop open long enough for it to finish executing.)
Is there a better way to aggregate into a structure like this, maybe using a library that provides more data reshaping options instead looping over UserNbr? (In R, I'd use the data.table and dplyr libraries.)
# loop over all UserNbr:
# consolidate specialty fields into dict-like sets (to remove redundant codes);
# output one row per user to new data frame
out_rows = list()
spcltycol = df_tmp.column.get_loc('Spclty')
all_UserNbr = df_tmp['UserNbr'].unique()
for user in all_UserNbr:
df_user = df_tmp.loc[df_tmp['UserNbr'] == user]
if df_user.shape[0] > 0:
open_combined = df_user_open.iloc[0, spcltycol] # capture 1st row
for row in range(1, df_user.shape[0]): # union with any subsequent rows
open_combined = open_combined.union(df_user.iloc[row, spcltycol])
new_row = df_user.drop(['Spclty', 'StartDt'], axis = 1).iloc[0].tolist()
new_row.append(open_combined)
out_rows.append(new_row)
# construct new dataframe with no redundant UserID rows:
df_out = pd.DataFrame(out_rows,
columns = ['UserNbr', 'Spclty'])
# convert Spclty sets to dicts:
df_out['Spclty'] = [dict(df_out['Spclty'][row]) for row in range(df_out.shape[0])]
The conversion to dict gets rid of specialties that are repeated between rows, In the output, a Spclty value should look like this:
{'104': '2010-01-31', '215': '2014-11-21', '352': '2016-07-13'}
except that there may be more key-value pairs than in any corresponding input row (resulting from aggregation over UserNbr).
I withdraw this question.
I had hoped there was an efficient way to use groupby with something else, but I haven't found any examples with a complex data structure like this one and have received no guidance.
For anyone who gets similarly stuck with very slow aggregation problems in Python, I suggest stepping up to PySpark. I am now tackling this problem with a Databricks notebook and am making headway with the pyspark.sql.window Window functions. (Now, it only takes minutes to run a test instead of hours!)
A partial solution is in the answer here:
PySpark list() in withColumn() only works once, then AssertionError: col should be Column
I'm trying to build a data-frame of time series data. I have to retrieve the data from an API and every (i,j) entry in the data-frame (where "i" is the row and "j" is the column) has to be iterated through and filled individually.
Here's an idea of the type of thing i'm trying to do (note the API i'm using doesn't have historical data for what i'm trying to analyze):
import pandas as pd
import numpy as np
import time
def retrievedata(string):
take string
do some stuff with api
return float
label_list = ['label1','label1','label1', etc...]
discrete_points = 720
df = pd.DataFrame(index=np.arange(0, discrete_points), columns=(i for i in label_list))
So at this point I've pre-allocated a data frame. What comes next is the issue.
Now, I want to iterate over it and assign values to every (i,j) entry in the data frame based on a function i wrote to pull data. Note that the function I wrote has to be specific to a certain column (as it is taking as input the column label). And on top of that, each row will have different values b/c it is time-series data.
EDIT: Yuck, I found a gross way to make it work:
for row in range(discrete_points):
for label in label_list:
df.at[row, label] = retrievedata(label)
This is obviously a non-pythonic, non-numpy, non-pandas way of doing things. So i'd like to find a nicer and more efficient/less computing power intensive way of doing this.
I'm assuming it's gonna have to be some combination of: iter.rows(); iter.tuples(); df.loc(); df.at()
I'm stumped though.
Any ideas?
I am importing data from a file, which is working correctly. I have appended the data from this file into 3 different lists, name, mark, mark2 although I don't understand how or if i can make a new list called total_marks and add a calculation appending mark + mark2 into total_marks. Tried looking about for help on this and couldn't find much relating to it. The plan is to actually add the two lists together and work out a percentage which the total marks would be 150.
To add the two lists item by item:
combined = []
for m1, m2 in zip(mark, mark2):
combined.append(m1+m2)
The zip function returns an item pair from the two lists for each pair in the lists.:
https://docs.python.org/3/library/functions.html#zip
Then you can perform the final operation this way:
final = []
for m in combined:
final.append(m/150*100)
As I said in my comment, I highly recommend that once you've gotten past learning the basics that you then take the time to learn two libraries: pandas and xlwings. These will greatly help your ability to interact between python and excel. An operation like you have here becomes much simpler once you learn pandas dataframes.
Here is a better way, using pandas.
import pandas
df = pandas.read_csv('Classmarks.csv', index_col = 'student_name', names = ('student_name', 'mark1', 'mark2'), header = None)
df['combined'] = df['mark1'] + df['mark2']
df['final'] = df['combined'] / 150 * 100
print(df)
Don't have to do any loops using pandas. And you can then write it back to a csv file:
df.to_csv('Classmarksout.csv')