I have big dataset of values as follow:
column "bigger" would be index of the first row with bigger "bsl" than "mb" from current row. I need to do it without loop as I need it to be done in less than a second. by loop it's over a minute.
For example for the first row (with index 74729) the bigger is going to be 74731. I know it can be done by linq in C# but I'm almost new in python.
here is another example:
here is text version:
index bsl mb bigger
74729 47091.89 47160.00 74731.0
74730 47159.00 47201.00 74735.0
74731 47196.50 47201.50 74735.0
74732 47186.50 47198.02 74735.0
74733 47191.50 47191.50 74735.0
74734 47162.50 47254.00 74736.0
74735 47252.50 47411.50 74736.0
74736 47414.50 47421.00 74747.0
74737 47368.50 47403.00 74742.0
74738 47305.00 47310.00 74742.0
74739 47292.00 47320.00 74742.0
74740 47302.00 47374.00 74742.0
74741 47291.47 47442.50 74899.0
74742 47403.50 47416.50 74746.0
74743 47354.34 47362.50 74746.0
I'm not sure how many rows you have, but if the number is reasonable, you can perform a pairwise comparison:
# get data as arrays
a = df['bsl'].to_numpy()
b = df['mb'].to_numpy()
idx = df.index.to_numpy()
# compare values and mask lower triangle
# to ensure comparing only the greater indices
out = np.triu(a>b[:,None]).argmax(1).astype(float)
# reindex to original indices
idx = idx[out]
# mask invalid indices
idx[out<np.arange(len(out))] = np.nan
df['bigger'] = idx
Output:
bsl mb bigger
0 1 2 2.0
1 2 4 6.0
2 3 3 5.0
3 2 1 3.0
4 3 5 NaN
5 4 2 5.0
6 5 1 6.0
7 1 0 7.0
Consider the following dataframe
df = pd.DataFrame()
df['Amount'] = [13,17,31,48]
I want to calculate for each row the std of the previous 2 values of the column "Amount". For example:
For the third row, the value should be the std of 17 and 13 (which is 2).
For the fourth row, the value should be the std of 31 and 17 (which is 7).
This is what I did:
df['std previous 2 weeks'] = df['Amount'].shift(1).rolling(2).std()
But this is not working. I thought that my problem was an index problem. But this works perfectly with the sum method.
df['total amount of previous 2 weeks'] = df['Amount'].shift(1).rolling(2).sum()
PD : I know that this can be done in some other ways but I want to know the reason for why this does not work (and how to fix it).
You could shift after rolling.std. Also the degrees of freedom is 1 by default, it seems you want it to be 0.
df['Stdev'] = df['Amount'].rolling(2).std(ddof=0).shift()
Output:
Amount Stdev
0 13 NaN
1 17 NaN
2 31 2.0
3 48 7.0
I need to calculate the percentile using a specific algorithm that is not available using either pandas.rank() or numpy.rank().
The ranking algorithm is calculated as follows for a series:
rank[i] = (# of values in series less than i + # of values equal to
i*0.5)/total # of values
so if I had the following series
s=pd.Series(data=[5,3,8,1,9,4,14,12,6,1,1,4,15])
For the first element, 5 there are 6 values less than 5 and no other values = to 5. The rank would be (6+0x0.5)/13 or 6/13.
For the fourth element (1) it would be (0+ 2x0.5)/13 or 1/13.
How could I calculate this without using a loop? I assume a combination of s.apply and/or s.where() but can't figure it out and have tried searching. I am looking to apply to the entire series at once, with the result being a series with the percentile ranks.
You could use numpy broadcasting. First convert s to a numpy column array. Then use numpy broadcasting to count the number of items less than i for each i. Then count the number of items equal to i for each i (note that we need to subract 1 since, i is equal to i itself). Finally add them and build a Series:
tmp = s.to_numpy()
s_col = tmp[:, None]
less_than_i_count = (s_col>tmp).sum(axis=1)
eq_to_i_count = ((s_col==tmp).sum(axis=1) - 1) * 0.5
ranks = pd.Series((less_than_i_count + eq_to_i_count) / len(s), index=s.index)
Output:
0 0.461538
1 0.230769
2 0.615385
3 0.076923
4 0.692308
5 0.346154
6 0.846154
7 0.769231
8 0.538462
9 0.076923
10 0.076923
11 0.346154
12 0.923077
dtype: float64
This is my first question on Stack Overflow, please let me know how I can help you help me if my question is unclear.
Goal: Use Python and Pandas to Outer join (or merge) Data Sets containing different experimental trials where the "x" axis of each trial is extremely similar but has some deviations. Most importantly, the "x" axis increases, hits a maximum and then decreases, often overlapping with previously existing "x" points.
Problem: When I go to join/merge the datasets on "x", the "x" column is sorted, messing up the order of the collected data and making it impossible to plot it correctly.
Here is a small example of what I am trying to do:
Wouldn't let me add pictures because I am new. Here is the code to generate these example data sets.
Data Sets :
Import:
import numpy as np
import pandas as pd
import random as rand
Code :
T1 = {'x':np.array([1,1.5,2,2.5,3,3.5,4,5,2,1]),'y':np.array([10000,8500,7400,6450,5670,5100,4600,4500,8400,9000]),'z':np.array(rand.sample(range(0,10000),10))}'
T2 = {'x':np.array([1,2,3,4,5,6,7,2,1.5,1]),'y':np.array([10500,7700,5500,4560,4300,3900,3800,5400,8400,8800]),'z':np.array(rand.sample(range(0,10000),10))}
Trial1 = pd.DataFrame(T1)
Trial2 = pd.DataFrame(T2)
Attempt to Merge/Join:
WomboCombo = Trial1.join(Trial2,how='outer',lsuffix=1,rsuffix=2, on='x')
WomboCombo2 = pd.merge(left=Trial1, right= Trial2, how = 'outer', left
Attempt to split into two parts, increasing and decreasing part (manually found row number where data "x" starts decreasing):
Trial1Inc = Trial1[0:8]
Trial2Inc = Trial2[0:7]
Result - Merge works well, join messes with the "x" column, not sure why:
Trial1Inc.merge(Trial2Inc,on='x',how='outer', suffixes=[1,2])
Incrementing section Merge Result
Trial1Inc.join(Trial2Inc,on='x',how='outer', lsuffix=1,rsuffix=2)
Incrementing section Join Result
Hopefully my example is clear, the "x" column in Trial 1 increases until 5, then decreases back towards 0. In Trial 2, I altered the test a bit because I noticed that I needed data at a slightly higher "x" value. Trial 2 Increases until 7 and then quickly decreases back towards 0.
My end goal is to plot the average of all y values (where there is overlap between the trials) against the corresponding x values.
If there is overlap I can add error bars. Pandas is almost perfect for what I am trying to do because an Outer join adds null values where there is no overlap and is capable of horizontally concatenating the two trials when there is overlap.
All thats left now is to figure out how to join on the "x" column but maintain its order of increasing values and then decreasing values. The reason it is important for me to first increase "x" and then decrease it is because when looking at the "y" values, it seems as though the initial "y" value at a given "x" is greater than the "y" value when "x" is decreasing (E.G. in trial 1 when x=1, y=10000, however, later in the trial when we come back to x=1, y=9000, this trend is important. When Pandas sorts the column before merging, instead of there being a clean curve showing a decrease in "y" as "x" increases and then the reverse, there are vertical downward jumps at any point where the data was joined.
I would really appreciate any help with either:
A) a perfect solution that lets me join on "x" when "x" contains duplicates
B) an efficient way to split the data sets into increasing "x" and decreasing "x" so that I can merge the increasing and decreasing sections of each trial separately and then vertically concat them.
Hopefully I did an okay job explaining the problem I would like to solve. Please let me know if I can clarify anything,
Thanks for the help!
I think #xyzjayne idea of splitting the dataframe is a great idea.
Splitting Trial1 and Trial2:
# index of max x value in Trial2
t2_max_index = Trial2.index[Trial2['x'] == Trial2['x'].max()].tolist()
# split Trial2 by max value
trial2_high = Trial2.loc[:t2_max_index[0]].set_index('x')
trial2_low = Trial2.loc[t2_max_index[0]+1:].set_index('x')
# index of max x value in Trial1
t1_max_index = Trial1.index[Trial1['x'] == Trial1['x'].max()].tolist()
# split Trial1 by max vlaue
trial1_high = Trial1.loc[:t1_max_index[0]].set_index('x')
trial1_low = Trial1.loc[t1_max_index[0]+1:].set_index('x')
Once we split the dataframes we join the highers together and the lowers together:
WomboCombo_high = trial1_high.join(trial2_high, how='outer', lsuffix='1', rsuffix='2', on='x').reset_index()
WomboCombo_low = trial1_low.join(trial2_low, how='outer', lsuffix='1', rsuffix='2', on='x').reset_index()
We now combine them toegther to have one dataframe WomboCombo
WomboCombo = WomboCombo_high.append(WomboCombo_low)
OUTPUT:
x y1 z1 y2 z2
0 1.0 10000.0 3425.0 10500.0 3061.0
1 1.5 8500.0 5059.0 NaN NaN
2 2.0 7400.0 2739.0 7700.0 7090.0
3 2.5 6450.0 9912.0 NaN NaN
4 3.0 5670.0 2099.0 5500.0 1140.0
5 3.5 5100.0 9637.0 NaN NaN
6 4.0 4600.0 7581.0 4560.0 9584.0
7 5.0 4500.0 8616.0 4300.0 3940.0
8 6.0 NaN NaN 3900.0 5896.0
9 7.0 NaN NaN 3800.0 6211.0
0 2.0 8400.0 3181.0 5400.0 9529.0
2 1.5 NaN NaN 8400.0 3260.0
1 1.0 9000.0 4280.0 8800.0 8303.0
One possible solution is to give you trial rows specific IDs an then merge on the IDs. Should keep the x values from being sorted.
Here's what I was trying out, but it doesn't address varying numbers of data points. I like gym-hh's answer, though it's not clear to me that you wanted two columns of y,z pairs. So you could combine his ideas and this code to get what you need.
Trial1['index1'] = Trial1.index
Trial2['index1'] = Trial2.index
WomboCombo = Trial1.append(Trial2)
WomboCombo.sort_values(by=['index1'],inplace=True)
WomboCombo
Output:
x y z index1
0 1.0 10000 7148 0
0 1.0 10500 2745 0
1 1.5 8500 248 1
1 2.0 7700 9505 1
2 2.0 7400 6380 2
2 3.0 5500 3401 2
3 2.5 6450 6183 3
3 4.0 4560 5281 3
4 3.0 5670 99 4
4 5.0 4300 8864 4
5 3.5 5100 5132 5
5 6.0 3900 7570 5
6 4.0 4600 9951 6
6 7.0 3800 7447 6
7 2.0 5400 3713 7
7 5.0 4500 3863 7
8 1.5 8400 8776 8
8 2.0 8400 1592 8
9 1.0 9000 2167 9
9 1.0 8800 782 9
Problem
I need to test the first digit of each number in a column for conditions.
Conditions
is the first digit of checkVar greater than 5
or
is the first digit of checkVar less than 2
then set newVar=1
Solution
One thought that I had was to convert to it a string, left strip the space, and then take [0], but i can't figure out the code.
perhaps something like,
df.ix[df.checkVar.str[0:1].str.contains('1'),'newVar']=1
It isn't what I want, and for some reason i get this error
invalid index to scalar variable.
testing my original variable i get values that should meet the condition
df.checkVar.value_counts()
301 62
1 15
2 5
999 3
dtype: int64
ideally it would look something like this:
checkVar newVar
NaN 1 nan
2 nan
3 nan
4 nan
5 301.0
6 301.0
7 301.0
8 301.0
9 301.0
10 301.0
11 301.0
12 301.0
13 301.0
14 1.0 1
15 1.0 1
UPDATE
My final solution, since actual problem was more complex
w = df.EligibilityStatusSP3.dropna().astype(str).str[0].astype(int)
v = df.EligibilityStatusSP2.dropna().astype(str).str[0].astype(int)
u = df.EligibilityStatusSP1.dropna().astype(str).str[0].astype(int)
t = df.EligibilityStatus.dropna().astype(str).str[0].astype(int) #get a series of the first digits of non-nan numbers
df['MCelig'] = ((t < 5)|(t == 9)|(u < 5)|(v < 5)|(w < 5)).astype(int)
df.MCelig = df.MCelig.fillna(0)
t = df.checkVar.dropna().astype(str).str[0].astype(int) #get a series of the first digits of non-nan numbers
df['newVar'] = ((t > 5) | (t < 2)).astype(int)
df.newVar = df.newVar.fillna(0)
this might be slightly better, unsure, but another, very similar way to approach it.
t = df.checkVar.dropna().astype(str).str[0].astype(int)
df['newVar'] = 0
df.newVar.update(((t > 5) | (t < 2)).astype(int))
It helpful to break up the steps a bit when you are uncertain how to proceed.
def checkvar(x):
s = str(x)
first_d = int(s[0])
if first_d < 2 or first_d > 5:
return 1
else:
return 0
Change the "else: return" value to whatever you want (e.g., "else: pass"). Also, if you want to create a new column:
*Update - I didn't notice the NaNs before. I see that you are still having problems even with the dropna(). Does the following work for you, like it does for me?
df = pd.DataFrame({'old_col': [None, None, None, 13, 75, 22, 51, 61, 31]})
df['new_col'] = df['old_col'].dropna().apply(checkvar)
df
If so Maybe the issue in your data is with the dtype of 'old_col'? Have you tried converting it to float first?
df['old_col'] = df['old_col'].astype('float')