I have a Dataframe of Names, Sex, Ages of individuals:
I would like to create a new Dataframe by sampling a fixed number of samples such that the average age of the new DataFrame is the same as the original DataFrame.
sample_df = pd.DataFrame({'Var':['A','B','C','D','E'] , 'Ages' : [22,35,43,18,NaN]})
sample_df
Out[410]:
Var Ages
0 A 22
1 B 35
2 C 43
3 D 18
4 E NaN
I would like to sample only 3 rows such that the age of 'E' is equal to the mean of A,B,C,D
Consider an indefinite iteration using while True then break after needs are met but depending on the variability of your data, this may take some time to process. Below builds a list of 100-row samples and breaks after ten samples are achieved.
samples = []
while True:
sample_df = df.sample(n = 100)
if sample_df['Age'].mean() == df['Age'].mean():
samples.append(sample_df)
if len(samples) == 10:
break
Related
Imagine I have the following data frame:
Product
Month 1
Month 2
Month 3
Month 4
Total
Stuff A
5
0
3
3
11
Stuff B
10
11
4
8
33
Stuff C
0
0
23
30
53
that can be constructed from:
df = pd.DataFrame({'Product': ['Stuff A', 'Stuff B', 'Stuff C'],
'Month 1': [5, 10, 0],
'Month 2': [0, 11, 0],
'Month 3': [3, 4, 23],
'Month 4': [3, 8, 30],
'Total': [11, 33, 53]})
This data frame shows the amount of units sold per product, per month.
Now, what I want to do is to create a new column called "Average" that calculates the average units sold per month. HOWEVER, notice in this example that Stuff C's values for months 1 and 2 are 0. This product was probably introduced in Month 3, so its average should be calculated based on months 3 and 4 only. Also notice that Stuff A's units sold in Month 2 were 0, but that does not mean the product was introduced in Month 3 since 5 units were sold in Month 1. That is, its average should be calculated based on all four months. Assume that the provided data frame may contain any number of months.
Based on these conditions, I have come up with the following solution in pseudo-code:
months = ["list of index names of months to calculate"]
x = len(months)
if df["Month 1"] != 0:
df["Average"] = df["Total"] / x
elif df["Month 2"] != 0:
df["Average"] = df["Total"] / x - 1
...
elif df["Month " + str(x)] != 0:
df["Average"] = df["Total"] / 1
else:
df["Average"] = 0
That way, the average would be calculated starting from the first month where units sold are different from 0. However, I haven't been able to translate this logical abstraction into actual working code. I couldn't manage to iterate over len(months) while maintaining the elif conditions. Or maybe there is a better, more practical approach.
I would appreciate any help, since I've been trying to crack this problem for a while with no success.
There is numpy method np.trim_zeros that trims leading and/or trailing zeros. Using a list comprehension, you can iterate over the relevant DataFrame rows, trim the leading zeros and find the average of what remains for each row.
Note that since 'Month 1' to 'Month 4' are consecutive, you can slice the columns between them using .loc.
import numpy as np
df['Average Sales'] = [np.trim_zeros(row, trim='f').mean() for row in df.loc[:, 'Month 1':'Month 4'].to_numpy()]
Output:
Product Month 1 Month 2 Month 3 Month 4 Total Average Sales
0 Stuff A 5 0 3 3 11 2.75
1 Stuff B 10 11 4 8 33 8.25
2 Stuff C 0 0 23 30 53 26.50
Try:
df = df.set_index(['Product','Total'])
df['Average'] = df.where(df.ne(0).cummax(axis=1)).mean(axis=1)
df_out=df.reset_index()
print(df_out)
Output:
Product Total Month 1 Month 2 Month 3 Month 4 Average
0 Stuff A 11 5 0 3 3 2.75
1 Stuff B 33 10 11 4 8 8.25
2 Stuff C 53 0 0 23 30 26.50
Details:
Move Product and Total into the dataframe index, so we can do calcation on the rest of the dataframe.
First create a boolean matrix using ne to zero. Then, use cummax along the rows which means that if there is a non-zero value, It will remain True until then end of the row. If it starts with a zero, then the False will stay until first non-zero then turns to Turn and remain True.
Next, use pd.DataFrame.where to only select those values for that boolean matrix were Turn, other values (leading zeros) will be NaN and not used in the calcuation of mean.
If you don't mind it being a little memory inefficient, you could put your dataframe into a numpy array. Numpy has a built-in function to remove zeroes from an array, and then you could use the mean function to calculate the average. It could look something like this:
import numpy as np
arr = np.array(Stuff_A_DF)
mean = arr[np.nonzero(arr)].mean()
Alternatively, you could manually extract the row to a list, then loop through to remove the zeroes.
I have a dataset that consists of around 33 variables. The dataset contains patient information and the outcome of interest is binary in nature. Below is a snippet of the data.
The dataset is stored as a pandas dataframe
df.head()
ID Age GAD PHQ Outcome
1 23 17 23 1
2 54 19 21 1
3 61 23 19 0
4 63 16 13 1
5 37 14 8 0
I want to run independent t-tests looking at the differences in patient information based on outcome. So, if I were to run a t-test for each alone, I would do:
age_neg_outcome = df.loc[df.outcome ==0, ['Age']]
age_pos_outcome = df.loc[df.outcome ==1, ['Age']]
t_age, p_age = stats.ttest_ind(age_neg_outcome ,age_pos_outcome, unequal = True)
print('\t Age: t= ', t_age, 'with p-value= ', p_age)
How can I do this in a for loop for each of the variables?
I've seen this post which is slightly similar but couldn't manage to use it.
Python : T test ind looping over columns of df
You are almost there. ttest_ind accepts multi-dimensional arrays too:
cols = ['Age', 'GAD', 'PHQ']
cond = df['outcome'] == 0
neg_outcome = df.loc[cond, cols]
pos_outcome = df.loc[~cond, cols]
# The unequal parameter is invalid so I'm leaving it out
t, p = stats.ttest_ind(neg_outcome, pos_outcome)
for i, col in enumerate(cols):
print(f'\t{col}: t = {t[i]:.5f}, with p-value = {p[i]:.5f}')
Output:
Age: t = 0.12950, with p-value = 0.90515
GAD: t = 0.32937, with p-value = 0.76353
PHQ: t = -0.96683, with p-value = 0.40495
I am trying to calculate median values on the fly based on multiple conditions in each row of a data frame and am not getting there.
Basically, for every row, I am counting the number of people in the same department with rank B with pay greater than the pay listed in that row. I was able to get the count to work properly with a lambda function:
df['B Count'] = df.apply(lambda x: sum(df[(df['Department'] == x['Department']) & (df['Rank'] == 'B')]['Pay'] > x['Pay']), axis=1)
However, I now need to calculate the median for each case satisfying those conditions. So in row x of the data frame, I need the median of df['Pay'] for all others matching x['Department'] and df['Rank'] == 'B'. I can't apply .median() instead of sum(), as that gives me the median count, not the median pay. Any thoughts?
Using the fake data below, the 'B Count' code from above counts the number of B's in each Department with higher pay than each A. That part works fine. What I want is to then construct the 'B Median' column, calculating the median pay of the B's in each Department with higher pay than each A in the same Department.
Person Department Rank Pay B Count B Median
1 One A 1000 1 1500
2 One B 800
3 One A 500 2 1150
4 One A 3000 0
5 One B 1500
6 Two B 2000
7 Two B 1800
8 Two A 1500 3 1800
9 Two B 1700
10 Two B 1000
Well, I was able to do what I wanted to do with a function:
def median_b(x):
if x['B Count'] == 0:
return np.nan
else:
return df[(df['Department'] == x['Department']) & (df['Rank'] == 'B') & (
df['Pay'] > x['Pay'])]['Pay'].median()
df['B Median'] = df.apply(median_b, axis = 1)
Do any of you know of better ways to achieve this result?
I am working with a database that looks like the below. For each fruit (just apple and pears below, for conciseness), we have:
1. yearly sales,
2. current sales,
3. monthly sales and
4.the standard deviation of sales.
Their ordering may vary, but it's always 4 values per fruit.
dataset = {'apple_yearly_avg': [57],
'apple_sales': [100],
'apple_monthly_avg':[80],
'apple_st_dev': [12],
'pears_monthly_avg': [33],
'pears_yearly_avg': [35],
'pears_sales': [40],
'pears_st_dev':[8]}
df = pd.DataFrame(dataset).T#tranpose
df = df.reset_index()#clear index
df.columns = (['Description', 'Value'])#name 2 columns
I would like to perform two sets of operations.
For the first set of operations, we isolate a fruit price, say 'pears', and subtract each average sales from current sales.
df_pear = df[df.loc[:, 'Description'].str.contains('pear')]
df_pear['temp'] = df_pear['Value'].where(df_pear.Description.str.contains('sales')).bfill()
df_pear ['some_op'] = df_pear['Value'] - df_pear['temp']
The above works, by creating a temporary column holding pear_sales of 40, backfill it and then use it to subtract values.
Question 1: is there a cleaner way to perform this operation without a temporary array? Also I do get the common warning saying I should use '.loc[row_indexer, col_indexer], even though the output still works.
For the second sets of operations, I need to add '5' rows equal to 'new_purchases' to the bottom of the dataframe, and then fill df_pear['some_op'] with sales * (1 + std_dev *some_multiplier).
df_pear['temp2'] = df_pear['Value'].where(df_pear['Description'].str.contains('st_dev')).bfill()
new_purchases = 5
for i in range(new_purchases):
df_pear = df_pear.append(df_pear.iloc[-1])#appends 5 copies of the last row
counter = 1
for i in range(len(df_pear)-1, len(df_pear)-new_purchases, -1):#backward loop from the bottom
df_pear.some_op.iloc[i] = df_pear['temp'].iloc[0] * (1 + df_pear['temp2'].iloc[i] * counter)
counter += 1
This 'backwards' loop achieves it, but again, I'm worried about readability since there's another temporary column created, and then the indexing is rather ugly?
Thank you.
I think, there is a cleaner way to perform your both tasks, for each
fruit in one go:
Add 2 columns, Fruit and Descr, the result of splitting of Description at the first "_":
df[['Fruit', 'Descr']] = df['Description'].str.split('_', n=1, expand=True)
To see the result you may print df now.
Define the following function to "reformat" the current group:
def reformat(grp):
wrk = grp.set_index('Descr')
sal = wrk.at['sales', 'Value']
dev = wrk.at['st_dev', 'Value']
avg = wrk.at['yearly_avg', 'Value']
# Subtract (yearly) average
wrk['some_op'] = wrk.Value - avg
# New rows
wrk2 = pd.DataFrame([wrk.loc['st_dev']] * 5).assign(
some_op=[ sal * (1 + dev * i) for i in range(5, 0, -1) ])
return pd.concat([wrk, wrk2]) # Old and new rows
Apply this function to each group, grouped by Fruit, drop Fruit
column and save the result back in df:
df = df.groupby('Fruit').apply(reformat)\
.reset_index(drop=True).drop(columns='Fruit')
Now, when you print(df), the result is:
Description Value some_op
0 apple_yearly_avg 57 0
1 apple_sales 100 43
2 apple_monthly_avg 80 23
3 apple_st_dev 12 -45
4 apple_st_dev 12 6100
5 apple_st_dev 12 4900
6 apple_st_dev 12 3700
7 apple_st_dev 12 2500
8 apple_st_dev 12 1300
9 pears_monthly_avg 33 -2
10 pears_sales 40 5
11 pears_yearly_avg 35 0
12 pears_st_dev 8 -27
13 pears_st_dev 8 1640
14 pears_st_dev 8 1320
15 pears_st_dev 8 1000
16 pears_st_dev 8 680
17 pears_st_dev 8 360
Edit
I'm in doubt whether Description should also be replicated to new
rows from "st_dev" row. If you want some other content there, set it
in reformat function, after wrk2 is created.
Given the following DataFrame:
import pandas as pd
import numpy as np
d=pd.DataFrame({' Label':['a','a','b','b'],'Count1':[10,20,30,40],'Count2':[20,45,10,35],
'Count3':[40,30,np.nan,22],'Nobs1':[30,30,70,70],'Nobs2':[65,65,45,45],
'Nobs3':[70,70,22,32]})
d
Label Count1 Count2 Count3 Nobs1 Nobs2 Nobs3
0 a 10 20 40.0 30 65 70
1 a 20 45 30.0 30 65 70
2 b 30 10 NaN 70 45 22
3 b 40 35 22.0 70 45 32
I would like to apply the z test for proportions on each combination of column groups (1 and 2, 1 and 3, 2 and 3) per row. By column group, I mean, for example, "Count1" and "Nobs1".
For example, one such test would be:
count = np.array([10, 20]) #from first row of Count1 and Count2, respectively
nobs = np.array([30, 65]) #from first row of Nobs1 and Nobs2, respectively
pv = proportions_ztest(count=count,nobs=nobs,value=0,alternative='two-sided')[1] #this returns just the p-value, which is of interest
pv
0.80265091465415639
I would want the result (pv) to go into a new column (first row) called "p_1_2" or something logical that corresponds to its respective columns.
In summary, here are the challenges I'm facing:
How to apply this per row.
...for each paired combination, mentioned above.
...where the column names and number of pairs of "Count" and "Nobs" columns may vary (assuming that there will always be a "Nobs" column for each "Count" column).
Related to 3: For example, I might have a column called "18-24" and another called "18-24_Nobs".
Thanks in advance!
To 1) and 2) for one test, additional tests can be coded similar or within an additonal loop
for i,row in d.iterrows():
d.loc[i,'test'] = proportions_ztest(count=row['Count1':'Count2'].values,
nobs=row['Nobs1':'Nobs2'].values,
value=0,alternative='two-sided')[1]
for 3) it should be possible the handle these case with pure python inside the loop