Only calculate mean of data rows in dataframe with no NaN-values - python

I have a dataframe with ID's of clients and their expenses for 2014-2018. What I want is to have the mean of the expenses for 2014-2018 of each ID in the dataframe.
There is however one condition: if one of the cells in the rows (2014-2018) is empty, NaN should be returned. So I only want the mean to be calculated when all 5 row-cells in the columns 2014-2018 have a numeric value.
Initial dataframe:
2014 2015 2016 2017 2018 ID
100 122.0 324 632 NaN 12.0
120 159.0 54 452 541.0 96.0
NaN 164.0 687 165 245.0 20.0
180 421.0 512 184 953.0 73.0
110 654.0 913 173 103.0 84.0
130 NaN 754 124 207.0 26.0
170 256.0 843 97 806.0 87.0
140 754.0 95 101 541.0 64.0
80 985.0 184 84 90.0 11.0
96 65.0 127 130 421.0 34.0
Desired output
2014 2015 2016 2017 2018 ID mean
100 122.0 324 632 NaN 12.0 NaN
120 159.0 54 452 541.0 96.0 265.20
NaN 164.0 687 165 245.0 20.0 NaN
180 421.0 512 184 953.0 73.0 450.00
110 654.0 913 173 103.0 84.0 390.60
130 NaN 754 124 207.0 26.0 NaN
170 256.0 843 97 806.0 87.0 434.40
140 754.0 95 101 541.0 64.0 326.20
80 985.0 184 84 90.0 11.0 284.60
96 65.0 127 130 421.0 34.0 167.80
Tried code: -> this however only gives me the mean, ignoring the NaN condition. Is their some brief lambda function that can add the condition to the code?
import pandas as pd
import numpy as np

data = pd.DataFrame({"ID": [12,96,20,73,84,26,87,64,11,34],
"2014": [100,120,np.nan,180,110,130,170,140,80,96],
"2015": [122,159,164,421,654,np.nan,256,754,985,65],
"2016": [324,54,687,512,913,754,843,95,184,127],
"2017": [632,452,165,184,173,124,97,101,84,130],
"2018": [np.nan,541,245,953,103,207,806,541,90,421]})


print(data)

fiveyear = ["2014", "2015", "2016", "2017", "2018"] -> if a cell in these rows is empty(NaN), then NaN should be in the new 'mean'-column. I only want the mean when, all 5 cells in the row have a numeric value.


data.loc[:, 'mean'] = data[fiveyear].mean(axis=1)

print(data)


Use dropna to remove rows before calculating the mean. Because pandas will align on index when assigning the result back, and these rows were removed, the result of these dropped rows is NaN
df['mean'] = df[fiveyear].dropna(how='any').mean(1)
Also possible to mask the result to only those rows that were all non-null
df['mean'] = df[fiveyear].mean(1).mask(df[fiveyear].isnull().any(1))
A bit more of a hack, but because you know you need all 5 values you could also use sum which supports the min_count argument, so anything with fewer than 5 values is NaN
df['mean'] = df[fiveyear].sum(1, min_count=len(fiveyear))/len(fiveyear)


This is the same as #ALollz answer but with a flexible way to detect all columns regardless of how many years there are in the df
#get years columns in a list
yearsCols= [c for c in df if c != 'ID']
#calculate mean
df['mean'] = df[yearsCols].dropna(how='any').mean(1)

Related

Pandas array filter NaN and keep the first value in group

I have the following pandas dataframe. There are many NaN but there are lots of NaN value (I skipped the NaN value to make it look shorter).
0 NaN
...
26 NaN
27 357.0
28 357.0
29 357.0
30 NaN
...
246 NaN
247 357.0
248 357.0
249 357.0
250 NaN
...
303 NaN
304 58.0
305 58.0
306 58.0
307 58.0
308 58.0
309 58.0
310 58.0
311 58.0
312 58.0
313 58.0
314 58.0
315 58.0
316 NaN
...
333 NaN
334 237.0
I would like to filter all the NaN value and also only keep the first value out of the NaN (e.g. from index 27-29 there are three values, I would like to keep the value indexed 27 and skip the 28 and 29 value). The targeted array should be as follows:
27 357.0
247 357.0
304 58.0
334 237.0
I am not sure how could I keep only the first value. Thanks in advance.

Take only values that aren't nan, but the value before them is nan:
df = df[df.col1.notna() & df.col1.shift().isna()]
Output:
col1
27 357.0
247 357.0
304 58.0
334 237.0
Assuming all values are greater than 0, we could also do:
df = df.fillna(0).diff()
df = df[df.col1.gt(0)]

You can find the continuous index and diff to get its first value
m = (df['col'].dropna()
.index.to_series()
.diff().fillna(2).gt(1)
.reindex(range(df.index.max()+1))
.fillna(False))
out = df[m]
print(out)
col
27 357.0
247 357.0
304 58.0
334 237.0

Calculate mean of data rows in dataframe with date-headers, dictated by a 'datetime'-column

I have a dataframe with ID's of clients and their expenses for 2014-2018. What I want is to have the mean of the expenses per ID but only the years before a certain date can be taken into account when calculating the mean value (so column 'Date' dictates which columns can be taken into account for the mean).
Example: for index 0 (ID: 12), the date states '2016-03-08', then the mean should be taken from the columns 'y_2014' and 'y_2015', so then for this index, the mean is 111.0.
If the date is too early (e.g. somewhere in 2014 or earlier in this case), then NaN should be returned (see index 6 and 9).
Initial dataframe:
y_2014 y_2015 y_2016 y_2017 y_2018 Date ID
0 100.0 122.0 324 632 NaN 2016-03-08 12
1 120.0 159.0 54 452 541.0 2015-04-09 96
2 NaN 164.0 687 165 245.0 2016-02-15 20
3 180.0 421.0 512 184 953.0 2018-05-01 73
4 110.0 654.0 913 173 103.0 2017-08-04 84
5 130.0 NaN 754 124 207.0 2016-07-03 26
6 170.0 256.0 843 97 806.0 2013-02-04 87
7 140.0 754.0 95 101 541.0 2016-06-08 64
8 80.0 985.0 184 84 90.0 2019-03-05 11
9 96.0 65.0 127 130 421.0 2014-05-14 34
Desired output:
y_2014 y_2015 y_2016 y_2017 y_2018 Date ID mean
0 100.0 122.0 324 632 NaN 2016-03-08 12 111.0
1 120.0 159.0 54 452 541.0 2015-04-09 96 120.0
2 NaN 164.0 687 165 245.0 2016-02-15 20 164.0
3 180.0 421.0 512 184 953.0 2018-05-01 73 324.25
4 110.0 654.0 913 173 103.0 2017-08-04 84 559.0
5 130.0 NaN 754 124 207.0 2016-07-03 26 130.0
6 170.0 256.0 843 97 806.0 2013-02-04 87 NaN
7 140.0 754.0 95 101 541.0 2016-06-08 64 447
8 80.0 985.0 184 84 90.0 2019-03-05 11 284.6
9 96.0 65.0 127 130 421.0 2014-05-14 34 NaN
Tried code: -> I'm still working on it, as I don't really know how to start for this, I only uploaded the dataframe so far, probably something with the 'datetime'-package has to be done to get the desired dataframe?
import pandas as pd
import numpy as np
import datetime
df = pd.DataFrame({"ID": [12,96,20,73,84,26,87,64,11,34],
"y_2014": [100,120,np.nan,180,110,130,170,140,80,96],
"y_2015": [122,159,164,421,654,np.nan,256,754,985,65],
"y_2016": [324,54,687,512,913,754,843,95,184,127],
"y_2017": [632,452,165,184,173,124,97,101,84,130],
"y_2018": [np.nan,541,245,953,103,207,806,541,90,421],
"Date": ['2016-03-08', '2015-04-09', '2016-02-15', '2018-05-01', '2017-08-04',
'2016-07-03', '2013-02-04', '2016-06-08', '2019-03-05', '2014-05-14']})
print(df)

Due to your naming convention, one need to extract the years from column names for comparison purpose. Then you can mask the data and taking mean:
# the years from columns
data = df.filter(like='y_')
data_years = data.columns.str.extract('(\d+)')[0].astype(int)
# the years from Date
years = pd.to_datetime(df.Date).dt.year.values
df['mean'] = data.where(data_years<years[:,None]).mean(1)
Output:
y_2014 y_2015 y_2016 y_2017 y_2018 Date ID mean
0 100.0 122.0 324 632 NaN 2016-03-08 12 111.00
1 120.0 159.0 54 452 541.0 2015-04-09 96 120.00
2 NaN 164.0 687 165 245.0 2016-02-15 20 164.00
3 180.0 421.0 512 184 953.0 2018-05-01 73 324.25
4 110.0 654.0 913 173 103.0 2017-08-04 84 559.00
5 130.0 NaN 754 124 207.0 2016-07-03 26 130.00
6 170.0 256.0 843 97 806.0 2013-02-04 87 NaN
7 140.0 754.0 95 101 541.0 2016-06-08 64 447.00
8 80.0 985.0 184 84 90.0 2019-03-05 11 284.60
9 96.0 65.0 127 130 421.0 2014-05-14 34 NaN

one more answer:
import pandas as pd
import numpy as np

df = pd.DataFrame({"ID": [12,96,20,73,84,26,87,64,11,34],
"y_2014": [100,120,np.nan,180,110,130,170,140,80,96],
"y_2015": [122,159,164,421,654,np.nan,256,754,985,65],
"y_2016": [324,54,687,512,913,754,843,95,184,127],
"y_2017": [632,452,165,184,173,124,97,101,84,130],
"y_2018": [np.nan,541,245,953,103,207,806,541,90,421],
"Date": ['2016-03-08', '2015-04-09', '2016-02-15', '2018-05-01', '2017-08-04',
'2016-07-03', '2013-02-04', '2016-06-08', '2019-03-05', '2014-05-14']})
#Subset from original df to calculate mean
subset = df.loc[:,['y_2014', 'y_2015', 'y_2016', 'y_2017', 'y_2018']]
#an expense value is only available for the calculation of the mean when that year has passed, therefore 2015-01-01 is chosen for the 'y_2014' column in the subset etc. to check with the 'Date'-column
subset.columns = ['2015-01-01', '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01']

s = subset.columns[0:].values < df.Date.values[:,None]
t = s.astype(float)
t[t == 0] = np.nan
df['mean'] = (subset.iloc[:,0:]*t).mean(1)

print(df)
#Additionally: (gives the sum of expenses before a certain date in the 'Date'-column
df['sum'] = (subset.iloc[:,0:]*t).sum(1)

print(df)

iterating over loc on dataframes

I'm trying to extract data from a list of dataframes and extract row ranges. Each dataframe might not have the same data, therefore I have a list of possible index ranges that I would like loc to loop over, i.e. from the code sample below, I might want CIN to LAN, but on another dataframe, the CIN row doesn't exist, so I will want DET to LAN or HOU to LAN.
so I was thinking putting them in a list and iterating over the list, i.e.
for df in dfs:
ranges=[[df.loc["CIN":"LAN"]], [df.loc["DET":"LAN"]]]
extracted ranges = (i for i in ranges)
I'm not sure how you would iterate over a list and feed into loc, or perhaps .query().
df1 stint g ab r h X2b X3b hr rbi sb cs bb \
year team
2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105
DET 5 301 1062 162 283 54 4 37 144.0 24.0 7.0 97
HOU 4 311 926 109 218 47 6 14 77.0 10.0 4.0 60
LAN 11 413 1021 153 293 61 3 36 154.0 7.0 5.0 114
NYN 13 622 1854 240 509 101 3 61 243.0 22.0 4.0 174
SFN 5 482 1305 198 337 67 6 40 171.0 26.0 7.0 235
TEX 2 198 729 115 200 40 4 28 115.0 21.0 4.0 73
TOR 4 459 1408 187 378 96 2 58 223.0 4.0 2.0 190
df2 so ibb hbp sh sf gidp
year team
2008 DET 176.0 3.0 10.0 4.0 8.0 28.0
HOU 212.0 3.0 9.0 16.0 6.0 17.0
LAN 141.0 8.0 9.0 3.0 8.0 29.0
NYN 310.0 24.0 23.0 18.0 15.0 48.0
SFN 188.0 51.0 8.0 16.0 6.0 41.0
TEX 140.0 4.0 5.0 2.0 8.0 16.0
TOR 265.0 16.0 12.0 4.0 16.0 38.0

Here is a solution:
import pandas as pd
# Prepare a list of ranges
ranges = [('CIN','LAN'), ('DET','LAN')]
# Declare an empty list of data frames and a list with the existing data frames
df_ranges = []
df_list = [df1, df2]
# Loop over multi-indices
for i, idx_range in enumerate(ranges):
df = df_list[i]
row1, row2 = idx_range
df_ranges.append(df.loc[(slice(None), slice(row1, row2)),:])
# Print the extracted data
print('Extracted data:\n')
print(df_ranges)
Output:
[ stint g ab r h X2b X3b hr rbi sb cs bb
year team
2007 CIN 6 379 745 101 203 35 2 36 125 10 1 105
DET 5 301 1062 162 283 54 4 37 144 24 7 97
HOU 4 311 926 109 218 47 6 14 77 10 4 60
LAN 11 413 1021 153 293 61 3 36 154 7 5 114
so ibb hbp sh sf gidp
year team
2008 DET 176 3 10 4 8 28
HOU 212 3 9 16 6 17
LAN 141 8 9 3 8 29]

Pandas: Fill missing values using last available

I have a dataframe as follows:
A B
zDate
01-JAN-17 100 200
02-JAN-17 111 203
03-JAN-17 NaN 202
04-JAN-17 109 205
05-JAN-17 101 211
06-JAN-17 105 NaN
07-JAN-17 104 NaN
What is the best way, to fill the missing values, using last available ones?
Following is the intended result:
A B
zDate
01-JAN-17 100 200
02-JAN-17 111 203
03-JAN-17 111 202
04-JAN-17 109 205
05-JAN-17 101 211
06-JAN-17 105 211
07-JAN-17 104 211

Use ffill function, what is same as fillna with method ffill:
df = df.ffill()
print (df)
A B
zDate
01-JAN-17 100.0 200.0
02-JAN-17 111.0 203.0
03-JAN-17 111.0 202.0
04-JAN-17 109.0 205.0
05-JAN-17 101.0 211.0
06-JAN-17 105.0 211.0
07-JAN-17 104.0 211.0

How to retrieve one column from csv file using python?

im trying to retrieve the age column from one of the csv file , here is what i coded so far.
df = pd.DataFrame.from_csv('train.csv')
result = df[(df.Sex=='female') & (df.Pclass==3)]
print(result.Age)
# finding the average age of all people who survived
print len(result)
sum = len(result)
I printed out the age, because i wanted to see the list of all ages that belong to the colunm of sex that has the value of "female" and the column of class which has the value of "3"
the print result for some reason shows the colunm number and the age next to it, i just want it print the list of ages thats all.
PassengerId
3 26.0
9 27.0
11 4.0
15 14.0
19 31.0
20 NaN
23 15.0
25 8.0
26 38.0
29 NaN
33 NaN
39 18.0
40 14.0
41 40.0
45 19.0
48 NaN
50 18.0
69 17.0
72 16.0
80 30.0
83 NaN
86 33.0
101 28.0
107 21.0
110 NaN
112 14.5
114 20.0
115 17.0
120 2.0
129 NaN
...
658 32.0
678 18.0
679 43.0
681 NaN
692 4.0
698 NaN
703 18.0
728 NaN
730 25.0
737 48.0
768 30.5
778 5.0
781 13.0
787 18.0
793 NaN
798 31.0
800 30.0
808 18.0
814 6.0
817 23.0
824 27.0
831 15.0
853 9.0
856 18.0
859 24.0
864 NaN
876 15.0
883 22.0
886 39.0
889 NaN
Name: Age, dtype: float64
This is what my program prints, i just want the list of age on the right column only not the passengerID column which is on the left.
Thank you

result.Age is a pandas Series object, and so when you print it, column headers, indices, and data types are shown as well. This is a good thing, because it makes the printed representation of the object much more useful.
If you want to control exactly how the data is displayed, you will need to do some string formatting. Something like this should do what you're asking for:
print('\n'.join(str(x) for x in result.Age))
If you want access to the raw data underlying that column for some reason (usually you can work with the Series just as well), without indices or headers, you can get a numpy array with
result.Age.values

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