I have a dataFrame really similar to that, but with thousands of values :
import numpy as np
import pandas as pd
# Setup fake data.
np.random.seed([3, 1415])
df = pd.DataFrame({
'Class': list('AAAAAAAAAABBBBBBBBBB'),
'type': (['short']*5 + ['long']*5) *2,
'image name': (['image01']*2 + ['image02']*2)*5,
'Value2': np.random.random(20)})
I was able to find a way to do a random sampling of 2 values per images, per Class and per Type with the following code :
df2 = df.groupby(['type', 'Class', 'image name'])[['Value2']].apply(lambda s: s.sample(min(len(s),2)))
I got the following result :
I'm looking for a way to subset that table to be able to randomly choose a random image ('image name') per type and per Class (and conserve the 2 values for the randomly selected image.
Excel Example of my desired output :
IIUC, the issue is that you do not want to groupby the column image name, but if that column is not included in the groupby, your will lose this column
You can first create the grouby object
gb = df.groupby(['type', 'Class'])
Now you can interate over the grouby blocks using list comprehesion
blocks = [data.sample(n=1) for _,data in gb]
Now you can concatenate the blocks, to reconstruct your randomly sampled dataframe
pd.concat(blocks)
Output
Class Value2 image name type
7 A 0.817744 image02 long
17 B 0.199844 image01 long
4 A 0.462691 image01 short
11 B 0.831104 image02 short
OR
You can modify your code and add the column image name to the groupby like this
df.groupby(['type', 'Class'])[['Value2','image name']].apply(lambda s: s.sample(min(len(s),2)))
Value2 image name
type Class
long A 8 0.777962 image01
9 0.757983 image01
B 19 0.100702 image02
15 0.117642 image02
short A 3 0.465239 image02
2 0.460148 image02
B 10 0.934829 image02
11 0.831104 image02
EDIT: Keeping image same per group
Im not sure if you can avoid using an iterative process for this problem. You could just loop over the groupby blocks, filter the groups taking a random image and keeping the same name per group, then randomly sample from the remaining images like this
import random
gb = df.groupby(['Class','type'])
ls = []
for index,frame in gb:
ls.append(frame[frame['image name'] == random.choice(frame['image name'].unique())].sample(n=2))
pd.concat(ls)
Output
Class Value2 image name type
6 A 0.850445 image02 long
7 A 0.817744 image02 long
4 A 0.462691 image01 short
0 A 0.444939 image01 short
19 B 0.100702 image02 long
15 B 0.117642 image02 long
10 B 0.934829 image02 short
14 B 0.721535 image02 short
Related
I have a variable whose name is Strike, in Strike variable values regularly change because it is under a loop.
This is my Dataframe
code example -
for i in range(len(df.strike)):
Strike = df.strike.iloc[i]
list1 = ['0-50', '50-100', '100-150'.......]
list2 = [2000, 132.4, 1467.40, ..........]
df = [] # Here i have to create dataframe
Strike contains values like - 33000, 33100, 33200, 33300....... so on it contains at least 145 values.
which I want to make rows.
And I have two list which is also changing from time to time because it is also under a loop.
list1 = ['0-50', '50-100', '100-150'.......]
list1 I want to make columns.
and list2 contains numeric values -
list2 = [2000, 132.4, 1467.40, ..........]
I need dataframe in this format.
List 1 should we column name, and list 2 should we values and strike variable should be rows.
but I don't understand how can I create this data frame.
IIUC you could use the DataFrame constructor directly with a reshaped numpy array as input:
# dummy example
list2 = list(range(4*7))
list1 = ['0-50', '50-100', '100-150', '150-200']
# replace by df.strike
strike = [33000, 33100, 33200, 33300, 33400, 33500, 33600]
df = pd.DataFrame(np.array(list2).reshape((-1, len(list1))),
index=strike, columns=list1)
output:
0-50 50-100 100-150 150-200
33000 0 1 2 3
33100 4 5 6 7
33200 8 9 10 11
33300 12 13 14 15
33400 16 17 18 19
33500 20 21 22 23
33600 24 25 26 27
I am working with a large dataset which I've stored in a pandas dataframe. All of my methods I've written to operate on this dataset work on dataframes, but some of them don't work on GroupBy objects.
I've come to a point in my code where I would like to group all data by author name (which I was able to achieve easily via .groupby()). Unfortunately, this outputs a GroupBy object which isn't very useful to me when I want to use dataframe only methods.
I've searched tons of other posts but not found any satisfying answer... how do I convert this GroupBy object back into a DataFrame? (Note: It is much too large for me to manually select groups and concatenate them into a dataframe, I need something automated).
Not exactly sure I understand, so if this isn't what you are looking for, please comment.
Creating a dataframe:
df = pd.DataFrame({'author':['gatsby', 'king', 'michener', 'michener','king','king', 'tolkein', 'gatsby'], 'b':range(13,21)})
author b
0 gatsby 13
1 king 14
2 michener 15
3 michener 16
4 king 17
5 king 18
6 tolkein 19
7 gatsby 20
#create the groupby object
dfg = df.groupby('author')
In [44]: dfg
Out[44]: <pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002169D24DB20>
#show groupby works using count()
dfg.count()
b
author
gatsby 2
king 3
michener 2
tolkein 1
But I think this is what you want. How to revert dfg back to a dataframe. You just need to perform some function on it that doesn't change the data. This is one way.
df_reverted = dfg.apply(lambda x: x)
author b
0 gatsby 13
1 king 14
2 michener 15
3 michener 16
4 king 17
5 king 18
6 tolkein 19
7 gatsby 20
This is another way and may be faster; note the dataframe names df and dfg.
df[dfg['b'].transform('count') > 0]
It's testing groupby and taking all groups greater than zero (so everything), returns a boolean series that is applied against the original dataframe, df
So I am trying to open a CSV file, read its fields and based on that fix some other fields and then save that data back to csv. My problem is that the CSV file has 2 million rows. What would be the best way to speed this up.
The CSV file consists of
ID; DATE(d/m/y); SPECIAL_ID; DAY; MONTH; YEAR
I am counting how often a row with the same date appears on my record and then update SPECIAL_ID based on that data.
Based on my previous research I decided to use pandas. I'll be processing even bigger sets of data in future (1-2GB) - this one is around 119MB so it crucial I find a good fast solution.
My code goes as follows:
df = pd.read_csv(filename, delimiter=';')
df_fixed= pd.DataFrame(columns=stolpci) #when I process the row in df I append it do df_fixed
d = 31
m = 12
y = 100
s = (y,m,d)
list_dates= np.zeros(s) #3 dimensional array.
for index, row in df.iterrows():
# PROCESSING LOGIC GOES HERE
# IT CONSISTS OF FEW IF STATEMENTS
list_dates[row.DAY][row.MONTH][row.YEAR] += 1
row['special_id'] = list_dates[row.DAY][row.MONTH][row.YEAR]
df_fixed = df_fixed.append(row.to_frame().T)
df_fixed .to_csv(filename_fixed, sep=';', encoding='utf-8')
I tried to make a print for every thousand rows processed. At first, my script needs 3 seconds for 1000 rows, but the longer it runs the slower it gets.
at row 43000 it needs 29 seconds and so on...
Thanks for all future help :)
EDIT:
I am adding additional information about my CSV and exptected output
ID;SPECIAL_ID;sex;age;zone;key;day;month;year
2;13012016505__-;F;1;1001001;1001001_F_1;13;1;2016
3;25122013505__-;F;4;1001001;1001001_F_4;25;12;2013
4;24022012505__-;F;5;1001001;1001001_F_5;24;2;2012
5;09032012505__-;F;5;1001001;1001001_F_5;9;3;2012
6;21082011505__-;F;6;1001001;1001001_F_6;21;8;2011
7;16082011505__-;F;6;1001001;1001001_F_6;16;8;2011
8;21102011505__-;F;6;1001001;1001001_F_6;16;8;2011
I have to replace - in the special ID field to a proper number.
For example for a row with
ID = 2 the SPECIAL_ID will be
26022018505001 (- got replaced by 001) if someone else in the CSV shares the same DAY, MONTH, YEAR the __- will be replaced by 002 and so on...
So exptected output for above rows would be
ID;SPECIAL_ID;sex;age;zone;key;day;month;year
2;13012016505001;F;1;1001001;1001001_F_1;13;1;2016
3;25122013505001;F;4;1001001;1001001_F_4;25;12;2013
4;24022012505001;F;5;1001001;1001001_F_5;24;2;2012
5;09032012505001;F;5;1001001;1001001_F_5;9;3;2012
6;21082011505001;F;6;1001001;1001001_F_6;21;8;2011
7;16082011505001;F;6;1001001;1001001_F_6;16;8;2011
8;21102011505002;F;6;1001001;1001001_F_6;16;8;2011
EDIT:
I changed my code to something like this: I fill list of dicts with data and then convert that list do dataframe and save as csv. This will take around 30minutes to complete
list_popravljeni = []
df = pd.read_csv(filename, delimiter=';')
df_dates = df.groupby(by=['dan_roj', 'mesec_roj', 'leto_roj']).size().reset_index()
for index, row in df_dates.iterrows():
df_candidates= df.loc[(df['dan_roj'] == dan_roj) & (df['mesec_roj'] == mesec_roj) & (df['leto_roj'] == leto_roj) ]
for index, row in df_candidates.iterrows():
vrstica = {}
vrstica['ID'] = row['identifikator']
vrstica['SPECIAL_ID'] = row['emso'][0:11] + str(index).zfill(2)
vrstica['day'] = row['day']
vrstica['MONTH'] = row['MONTH']
vrstica['YEAR'] = row['YEAR']
list_popravljeni.append(vrstica)
pd.DataFrame(list_popravljeni, columns=list_popravljeni[0].keys())
I think this gives what you're looking for and avoids looping. Potentially it could be more efficient (I wasn't able to find a way to avoid creating counts). However, it should be much faster than your current approach.
df['counts'] = df.groupby(['year', 'month', 'day'])['SPECIAL_ID'].cumcount() + 1
df['counts'] = df['counts'].astype(str)
df['counts'] = df['counts'].str.zfill(3)
df['SPECIAL_ID'] = df['SPECIAL_ID'].str.slice(0, -3).str.cat(df['counts'])
I added a fake record at the end to confirm it does increment properly:
SPECIAL_ID sex age zone key day month year counts
0 13012016505001 F 1 1001001 1001001_F_1 13 1 2016 001
1 25122013505001 F 4 1001001 1001001_F_4 25 12 2013 001
2 24022012505001 F 5 1001001 1001001_F_5 24 2 2012 001
3 09032012505001 F 5 1001001 1001001_F_5 9 3 2012 001
4 21082011505001 F 6 1001001 1001001_F_6 21 8 2011 001
5 16082011505001 F 6 1001001 1001001_F_6 16 8 2011 001
6 21102011505002 F 6 1001001 1001001_F_6 16 8 2011 002
7 21102012505003 F 6 1001001 1001001_F_6 16 8 2011 003
If you want to get rid of counts, you just need:
df.drop('counts', inplace=True, axis=1)
DataFrame1:
Device MedDescription Quantity
RWCLD Acetaminophen (TYLENOL) 325 mg Tab 54
RWCLD Ampicillin Inj (AMPICILLIN) 2 g Each 13
RWCLD Betamethasone Inj *5mL* (CELESTONE SOLUSPAN) 30 mg (5 mL) Each 2
RWCLD Calcium Carbonate Chew (500mg) (TUMS) 200 mg Tab 17
RWCLD Carboprost Inj *1mL* (HEMABATE) 250 mcg (1 mL) Each 5
RWCLD Chlorhexidine Gluc Liq *UD* (PERIDEX/PERIOGARD) 0.12 % (15 mL) Each 5
Data Frame2:
Device DrwSubDrwPkt MedDescription BrandName MedID PISAlternateID CurrentQuantity Min Max StandardStock ActiveOrders DaysUnused
RWC-LD RWC-LD_MAIN Drw 1-Pkt 12 Mag/AlOH/Smc 200-200-20/5 *UD* (MYLANTA/MAALOX) (30 mL) Each MYLANTA/MAALOX A03518 27593 7 4 10 N Y 3
RWC-LD RWC-LD_MAIN Drw 1-Pkt 20 ceFAZolin in Dextrose(ISO-OS) (ANCEF/KEFZOL) 1 g (50 mL) Each ANCEF/KEFZOL A00984 17124 6 5 8 N N 2
RWC-LD RWC-LD_MAIN Drw 1-Pkt 22 Clindamycin Phosphate/D5W (CLEOCIN) 900 mg (50 mL) IV Premix CLEOCIN A02419 19050 7 6 8 N N 2
What I want to do is append DataFrame2 values to Data Frame 1 ONLY if the 'MedDescription' matches. When it find the match, I would like to add only certain columns from dataFrame2[Min,Max,Days Unused] which are all integers
I had an iterative solution where I access the dataframe 1 object 1 row at a time and then check for a match with dataframe 2, once found I append the column numbers from there to the original dataFrame.
Is there a better way? It is making my computer slow to a crawl as I have thousands upon thousands of rows.
It sounds like you want to merge the target columns ('MedDescription', 'Min', 'Max', 'Days Unused') to df1 based on a matching 'MedDescription'.
I believe the best way to do this is as follows:
target_cols = ['MedDescription', 'Min', 'Max', 'Days Unused']
df1.merge(df2[target_cols], on='MedDescription', how='left')
how='left' ensures that all the data in df1 is returned, and only the target columns in df2 are appended if MedDescription matches.
Note: It is easier for others if you copy the results of df1/df2.to_dict(). The data above is difficult to parse.
This sounds like an opportunity to use Pandas' built-in functions for joining datasets - you should be able to join on MedDescription with a the desired columns from DataFrame2. The join function in Pandas is very efficient, and should far outperform your method of looping through.
Pandas has documentation on merging datasets that includes some good examples, and you can find ample literature on the concepts of joins in SQL tutorials.
pd.merge(ld,ldAc,on='MedDescription',how='outer')
This is the way I used to join the 2 DataFrames, it seems to work, although it deleted one of the Indexes that contained the devices.
I'm quite new to pandas and python, and I'm coming from a background in biochemistry and drug discovery. One frequent task that I'd like to automate is the conversion of a list of combination of drug treatments and proteins to a format that contains all such combinations.
For instance, if I have a DataFrame containing a given set of combinations:
https://github.com/colinhiggins/dillydally/blob/master/input.csv, I'd like to turn it into https://github.com/colinhiggins/dillydally/blob/master/output.csv such that each protein (1, 2, and 3) are copied n times to an output DataFrame where the number of rows, n, is the number of drugs and drug concentrations plus one for a no-drug row of each protein.
Ideally, the degree of combination would be dictated by some other table that indicates relationships, for example if proteins 1 and 2 are to be treated with drugs 1, 2, and 3 but that protein 2 isn't treated with any drugs.
I'm thinking some kind of nested for loop is going to be required, but I can't wrap my head around just quite how to start it.
Consider the following solution
from itertools import product
import pandas
protein = ['protein1' , 'protein2' , 'protein3' ]
drug = ['drug1' , 'drug2', 'drug3']
drug_concentration = [100,30,10]
df = pandas.DataFrame.from_records( list( i for i in product(protein, drug, drug_concentration ) ) , columns=['protein' , 'drug' , 'drug_concentration'] )
>>> df
protein drug drug_concentration
0 protein1 drug1 100
1 protein1 drug1 30
2 protein1 drug1 10
3 protein1 drug2 100
4 protein1 drug2 30
5 protein1 drug2 10
6 protein1 drug3 100
7 protein1 drug3 30
8 protein1 drug3 10
9 protein2 drug1 100
10 protein2 drug1 30
11 protein2 drug1 10
12 protein2 drug2 100
13 protein2 drug2 30
14 protein2 drug2 10
15 protein2 drug3 100
16 protein2 drug3 30
17 protein2 drug3 10
18 protein3 drug1 100
19 protein3 drug1 30
20 protein3 drug1 10
21 protein3 drug2 100
22 protein3 drug2 30
23 protein3 drug2 10
24 protein3 drug3 100
25 protein3 drug3 30
26 protein3 drug3 10
This is basically a cartesian product you're after, which is the functionality of the product function in the itertools module. I'm admitedly confused why you want the empty rows that just list out the proteins with nan's in the other columns. Not sure if that was intentional or accidental. If the datatypes were uniform and numeric this is similar functionality to what's known as a meshgrid.
I've worked through part of this with the help of add one row in a pandas.DataFrame using the method recommended by ShikharDua of creating a list of dicts, each dict corresponding to a row in the eventual DataFrame.
The code is:
data = pandas.read_csv('input.csv')
dict1 = {"protein":"","drug":"","drug_concentration":""} #should be able to get this automatically using the dataframe columns, I think
rows_list = []
for unique_protein in data.protein.unique():
dict1 = {"protein":unique_protein,"drug":"","drug_concentration":""}
rows_list.append(dict1)
for unique_drug in data.drug.unique():
for unique_drug_conc in data.drug_concentration.unique():
dict1 = {"protein":unique_protein,"drug":unique_drug,"drug_concentration":unique_drug_conc}
rows_list.append(dict1)
df = pandas.DataFrame(rows_list)
df
It isn't as flexible as I was hoping, since the extra row from protein with no drugs is hard-coded into the nested for loops, but at least its a start. I guess I can add some if statements within each for loop.
I've improved upon the earlier version
enclosed it in a function
added a check for proteins that won't be treated with drugs from another input CSV file that contains the same proteins in column A and either true or false in column B labeled "treat with drugs"
Skips null values. I noticed that my example input.csv had equal length columns, and the function started going a little nuts with NaN rows if they had unequal lengths.
Initial dictionary keys are set from the columns from the initial input CSV instead of hard-coding them.
I tested this with some real data (hence the change from input.csv to realinput.csv), and it works quite nicely.
Code for a fully functional python file follows:
import pandas
import os
os.chdir("path_to_directory_containing_realinput_and_boolean_file")
realinput = pandas.read_csv('realinput.csv')
rows_list = []
dict1 = dict.fromkeys(realinput.columns,"")
prot_drug_bool = pandas.read_csv('protein_drug_bool.csv')
prot_drug_bool.index = prot_drug_bool.protein
prot_drug_bool = prot_drug_bool.drop("protein",axis=1)
def null_check(value):
return pandas.isnull(value)
def combinator(input_table):
for unique_protein in input_table.protein.unique():
dict1 = dict.fromkeys(realinput.columns,"")
dict1['protein']=unique_protein
rows_list.append(dict1)
if prot_drug_bool.ix[unique_protein]:
for unique_drug in input_table.drug.unique():
if not null_check(unique_drug):
for unique_drug_conc in input_table.drug_concentration.unique():
if not null_check(unique_drug_conc):
dict1 = dict.fromkeys(realinput.columns,"")
dict1['protein']=unique_protein
dict1['drug']=unique_drug
dict1['drug_concentration']=unique_drug_conc
rows_list.append(dict1)
df = pandas.DataFrame(rows_list)
return df
df2 = combinator(realinput)
df2.to_csv('realoutput.csv')
I'd still like to make it more versatile by getting away from hard-coding any dictionary keys and letting the user-defined input.csv column headers dictate the output. Also, I'd like to move away from the defined three-column setup to handle any number of columns.