Error in getting specific row from python pandas - python

I want to extract a row by name from the foll. dataframe:
Unnamed: 1 1 1.1 2 TOT
0
1 DEPTH(m) 0.01 1.24 1.52 NaN
2 BD 33kpa(t/m3) 1.60 1.60 1.60 NaN
3 SAND(%) 42.10 42.10 65.10 NaN
4 SILT(%) 37.90 37.90 16.90 NaN
5 CLAY(%) 20.00 20.00 18.00 NaN
6 ROCK(%) 12.00 12.00 12.00 NaN
7 WLS(kg/ha) 2.60 8.20 0.10 10.9
8 WLM(kg/ha) 5.00 8.30 0.00 13.4
9 WLSL(kg/ha) 0.00 3.80 0.10 3.9
10 WLSC(kg/ha) 1.10 3.50 0.00 4.6
11 WLMC(kg/ha) 2.10 3.50 0.00 5.6
12 WLSLC(kg/ha) 0.00 1.60 0.00 1.6
13 WLSLNC(kg/ha) 1.10 1.80 0.00 2.9
14 WBMC(kg/ha) 3.40 835.10 195.20 1033.7
15 WHSC(kg/ha) 66.00 8462.00 1924.00 10451.0
16 WHPC(kg/ha) 146.00 18020.00 4102.00 22269.0
17 WOC(kg/ha) 219.00 27324.00 6221.00 34.0
18 WLSN(kg/ha) 0.00 0.00 0.00 0.0
19 WLMN(kg/ha) 0.00 0.10 0.00 0.1
20 WBMN(kg/ha) 0.50 92.60 19.30 112.5
21 WHSN(kg/ha) 7.00 843.00 191.00 1041.0
22 WHPN(kg/ha) 15.00 1802.00 410.00 2227.0
23 WON(kg/ha) 22.00 2738.00 621.00 3381.0
I want to extract the row containing info on WOC(kg/ha). here is what I am doing:
df.loc['WOC(kg/ha)']
but I get the error:
*** KeyError: 'the label [WOC(kg/ha)] is not in the [index]'


You don't have that label in your index, it's in your first column the following should work:
df.loc[df['Unnamed: 1'] == 'WOC(kg/ha)']
otherwise set the index to that column and your code would work fine:
df.set_index('Unnamed: 1', inplace=True)
Also, this can be used to set index without explicitly specifying column name: df.set_index(df.columns[0], inplace=True)

Related

Value Error: Math Domain Error! I want to get the values of angles from sine equation

#How could I fix this problem? Your support will be highly appreciated. Thank you.
import pandas as pd
liquid_data='component.csv'
df1=pd.read_csv(liquid_data)
df1
YSP=1
YSN=0.62
YSLW=42.94
Ys=YSLW+2*(YSP*YSN)**0.5
print(Ys)
df1.YL
N=len(df1.YL)
print(N)
import numpy as np
YSL=np.zeros(N)
YL=np.zeros(N)
YLLW=np.zeros(N)
YLN=np.zeros(N)
YLP=np.zeros(N)
for i in range(0,N):
YSL[i]=Ys+df1.YL[i]-2*((YSLW*df1.YLLW[i])**0.5+(YSP*df1.YLN[i])**0.5+(YSN*df1.YLP[i])**0.5)
print(YSL)
import cmath
Radian=np.zeros(N)
Theta=np.zeros(N)
Ys=44.51
YL=np.zeros(N)
YSL=np.zeros(N)
import math
for i in range (N):
Radian[i]=math.asin((Ys-YSL[i])/YL[i])
print(Radian)
#liquid_data
Liquid YLLW YLP YLN YL
0 1 28.85 0.00 2.7 28.85
1 2 50.80 0.00 0.0 50.80
2 3 25.35 0.00 0.0 25.35
3 4 18.80 0.02 68.0 22.40
4 5 25.20 0.00 13.1 25.20
5 6 29.00 1.90 47.0 48.00
6 7 39.00 2.28 39.6 58.00
7 8 34.00 3.92 57.4 64.00
8 9 20.14 0.00 0.0 20.14
9 10 18.20 0.06 77.0 22.50
10 11 27.50 0.00 18.0 27.50
11 12 21.80 25.50 25.5 72.80

How do you reshape a csv file in python using pandas?

I'm trying to reshape this csv file in python to have 1,000 rows and all 12 columns but it shows up as only one column
I've tried using df.iloc[:][:1000] it shortens it to 1000 rows but it still only gives me 1 column
Dataframe for Wine
df_w= pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv')
df_w= df_w.iloc[:12][:1000]
df_w
The dataframe that shows up is 1000 rows with only 1 column and I'm wondering how you set the data into its respective column title

Use sep=';', for semicolon delimiter:
df_w= pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', sep=';')
df_w= df_w.iloc[:12][:1000]
df_w
Output:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
1 7.8 0.88 0.00 2.6 0.098 25.0 67.0 0.9968 3.20 0.68 9.8 5
2 7.8 0.76 0.04 2.3 0.092 15.0 54.0 0.9970 3.26 0.65 9.8 5
3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.9980 3.16 0.58 9.8 6
4 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
5 7.4 0.66 0.00 1.8 0.075 13.0 40.0 0.9978 3.51 0.56 9.4 5
6 7.9 0.60 0.06 1.6 0.069 15.0 59.0 0.9964 3.30 0.46 9.4 5
7 7.3 0.65 0.00 1.2 0.065 15.0 21.0 0.9946 3.39 0.47 10.0 7
8 7.8 0.58 0.02 2.0 0.073 9.0 18.0 0.9968 3.36 0.57 9.5 7
9 7.5 0.50 0.36 6.1 0.071 17.0 102.0 0.9978 3.35 0.80 10.5 5
10 6.7 0.58 0.08 1.8 0.097 15.0 65.0 0.9959 3.28 0.54 9.2 5
11 7.5 0.50 0.36 6.1 0.071 17.0 102.0 0.9978 3.35 0.80 10.5 5

Pandas: Sum current and following cell in Pandas DataFrame

Python code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import data as wb
stock='3988.HK'
df = wb.DataReader(stock,data_source='yahoo',start='2018-07-01')
rsi_period = 14
chg = df['Close'].diff(1)
gain = chg.mask(chg<0,0)
df['Gain'] = gain
loss = chg.mask(chg>0,0)
df['Loss'] = loss
avg_gain = gain.ewm(com = rsi_period-1,min_periods=rsi_period).mean()
avg_loss = loss.ewm(com = rsi_period-1,min_periods=rsi_period).mean()
df['Avg Gain'] = avg_gain
df['Avg Loss'] = avg_loss
rs = abs(avg_gain/avg_loss)
rsi = 100-(100/(1+rs))
df['RSI'] = rsi
df.reset_index(inplace=True)
df
Output:
Date High Low Open Close Volume Adj Close Gain Loss Avg Gain Avg Loss RSI
0 2018-07-03 3.87 3.76 3.83 3.84 684899302.0 3.629538 NaN NaN NaN NaN NaN
1 2018-07-04 3.91 3.84 3.86 3.86 460325574.0 3.648442 0.02 0.00 NaN NaN NaN
2 2018-07-05 3.70 3.62 3.68 3.68 292810499.0 3.680000 0.00 -0.18 NaN NaN NaN
3 2018-07-06 3.72 3.61 3.69 3.67 343653088.0 3.670000 0.00 -0.01 NaN NaN NaN
4 2018-07-09 3.75 3.68 3.70 3.69 424596186.0 3.690000 0.02 0.00 NaN NaN NaN
5 2018-07-10 3.74 3.70 3.71 3.71 327048051.0 3.710000 0.02 0.00 NaN NaN NaN
6 2018-07-11 3.65 3.61 3.63 3.64 371355401.0 3.640000 0.00 -0.07 NaN NaN NaN
7 2018-07-12 3.69 3.63 3.66 3.66 309888328.0 3.660000 0.02 0.00 NaN NaN NaN
8 2018-07-13 3.69 3.62 3.69 3.63 261928758.0 3.630000 0.00 -0.03 NaN NaN NaN
9 2018-07-16 3.63 3.57 3.61 3.62 306970074.0 3.620000 0.00 -0.01 NaN NaN NaN
10 2018-07-17 3.62 3.56 3.62 3.58 310294921.0 3.580000 0.00 -0.04 NaN NaN NaN
11 2018-07-18 3.61 3.55 3.58 3.58 334592695.0 3.580000 0.00 0.00 NaN NaN NaN
12 2018-07-19 3.61 3.56 3.61 3.56 211984563.0 3.560000 0.00 -0.02 NaN NaN NaN
13 2018-07-20 3.64 3.52 3.57 3.61 347506394.0 3.610000 0.05 0.00 NaN NaN NaN
14 2018-07-23 3.65 3.57 3.59 3.62 313125328.0 3.620000 0.01 0.00 0.010594 -0.021042 33.487100
15 2018-07-24 3.71 3.60 3.60 3.68 367627204.0 3.680000 0.06 0.00 0.015854 -0.018802 45.745967
16 2018-07-25 3.73 3.68 3.72 3.69 270460990.0 3.690000 0.01 0.00 0.015252 -0.016868 47.483263
17 2018-07-26 3.73 3.66 3.72 3.69 234388072.0 3.690000 0.00 0.00 0.013731 -0.015186 47.483263
18 2018-07-27 3.70 3.66 3.68 3.69 190039532.0 3.690000 0.00 0.00 0.012399 -0.013713 47.483263
19 2018-07-30 3.72 3.67 3.68 3.70 163971848.0 3.700000 0.01 0.00 0.012172 -0.012417 49.502851
20 2018-07-31 3.70 3.66 3.67 3.68 168486023.0 3.680000 0.00 -0.02 0.011047 -0.013118 45.716244
21 2018-08-01 3.72 3.66 3.71 3.68 199801191.0 3.680000 0.00 0.00 0.010047 -0.011930 45.716244
22 2018-08-02 3.68 3.59 3.66 3.61 307920738.0 3.610000 0.00 -0.07 0.009155 -0.017088 34.884632
23 2018-08-03 3.62 3.57 3.59 3.61 184816985.0 3.610000 0.00 0.00 0.008356 -0.015596 34.884632
24 2018-08-06 3.66 3.60 3.62 3.61 189696153.0 3.610000 0.00 0.00 0.007637 -0.014256 34.884632
25 2018-08-07 3.66 3.61 3.63 3.65 216157642.0 3.650000 0.04 0.00 0.010379 -0.013048 44.302922
26 2018-08-08 3.66 3.61 3.65 3.63 215365540.0 3.630000 0.00 -0.02 0.009511 -0.013629 41.101805
27 2018-08-09 3.66 3.59 3.59 3.65 230275455.0 3.650000 0.02 0.00 0.010378 -0.012504 45.353992
28 2018-08-10 3.66 3.60 3.65 3.62 219157328.0 3.620000 0.00 -0.03 0.009530 -0.013933 40.617049
29 2018-08-13 3.59 3.54 3.58 3.56 270620120.0 3.560000 0.00 -0.06 0.008759 -0.017658 33.158019
In this case, i want to create a new column 'max close within 14 trade days'.
'max close within 14 trade days' = Maximum 'close' within next 14 days.
For example,
in row 0, the data range should be from row 1 to row 15,
'max close within 14 trade days' = 3.86

You can do the following:
# convert to date time
df['Date'] = pd.to_datetime(df['Date'])
# calculate max for 14 days
df['max_close_within_14_trade_days'] = df['Date'].map(df.groupby([pd.Grouper(key='Date', freq='14D')])['Close'].max())
# fill missing values by previous value
df['max_close_within_14_trade_days'].fillna(method='ffill', inplace=True)
Date High Low Open Close max_close_within_14_trade_days
0 0 2018-07-03 3.87 3.76 3.83 3.86
1 1 2018-07-04 3.91 3.84 3.86 3.86
2 2 2018-07-05 3.70 3.62 3.68 3.86
3 3 2018-07-06 3.72 3.61 3.69 3.86
4 4 2018-07-09 3.75 3.68 3.70 3.86
Other solution:
df['max_close_within_14_trade_days'] = [df.loc[x+1:x+14,'Close'].max() for x in range(0, df.shape[0])]

Data split over 2 rows for each row entry - read in with pandas

I'm dealing with a dataset where each 'entry' is split over many rows which are different sizes,
i.e.
yyyymmdd hhmmss lat lon name nprt depth ubas udir cabs cdir
hs tp lp theta sp wf
20140701 000000 -76.500 208.000 'grid_point' 1 332.2 2.8 201.9 0.00 0.0
0 0.10 1.48 3.40 183.19 30.16 0.89
1 0.10 1.48 3.40 183.21 29.66 0.90
20140701 000000 -74.500 251.000 'grid_point' 1 1.0 8.4 159.7 0.00 0.0
0 0.63 4.24 28.02 105.05 32.71 0.85
1 0.60 4.21 27.68 110.42 27.04 0.95
2 0.20 5.78 52.18 43.73 17.98 0.01
3 0.06 6.55 66.86 176.86 11.04 0.10
20140701 000000 -74.500 258.000 'grid_point' 0 1.0 7.7 137.0 0.00 0.0
0 0.00 0.00 0.00 0.00 0.00 0.00
I'm only interested in the rows that begin with a date so the rest can be discarded. However, the number of additional rows varies throughout the data set (see code snippet for an example).
Ideally, I'd like to use pandas read_csv but I'm open to suggestions if that's not possible/there are easier ways.
So my question is how do you read data into a dataframe where the row begins with a date?
Thanks

You can use read_csv first, then try cast first and second column to_datetime with parameter errors='coerce' - it add NaT where are not dates. So last need filter rows with boolean indexing and mask created by notnull:
import pandas as pd
from pandas.compat import StringIO
temp=u"""yyyymmdd hhmmss lat lon name nprt depth ubas udir cabs cdir
hs tp lp theta sp wf
20140701 000000 -76.500 208.000 'grid_point' 1 332.2 2.8 201.9 0.00 0.0
0 0.10 1.48 3.40 183.19 30.16 0.89
1 0.10 1.48 3.40 183.21 29.66 0.90
20140701 000000 -74.500 251.000 'grid_point' 1 1.0 8.4 159.7 0.00 0.0
0 0.63 4.24 28.02 105.05 32.71 0.85
1 0.60 4.21 27.68 110.42 27.04 0.95
2 0.20 5.78 52.18 43.73 17.98 0.01
3 0.06 6.55 66.86 176.86 11.04 0.10
20140701 000000 -74.500 258.000 'grid_point' 0 1.0 7.7 137.0 0.00 0.0
0 0.00 0.00 0.00 0.00 0.00 0.00"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp), delim_whitespace=True)
print (pd.to_datetime(df.iloc[:,0] + df.iloc[:,1], errors='coerce', format='%Y%m%d%H%M%S'))
0 NaT
1 2014-07-01
2 NaT
3 NaT
4 2014-07-01
5 NaT
6 NaT
7 NaT
8 NaT
9 2014-07-01
10 NaT
dtype: datetime64[ns]
mask = pd.to_datetime(df.iloc[:,0] +
df.iloc[:,1], errors='coerce', format='%Y%m%d%H%M%S')
.notnull()
print (mask)
print (mask)
0 False
1 True
2 False
3 False
4 True
5 False
6 False
7 False
8 False
9 True
10 False
dtype: bool
print (df[mask])
yyyymmdd hhmmss lat lon name nprt depth ubas udir \
1 20140701 000000 -76.500 208.000 'grid_point' 1 332.2 2.8 201.9
4 20140701 000000 -74.500 251.000 'grid_point' 1 1.0 8.4 159.7
9 20140701 000000 -74.500 258.000 'grid_point' 0 1.0 7.7 137.0
cabs cdir
1 0.0 0.0
4 0.0 0.0

Reading csv file using pandas where columns are separated by varying amounts of whitespace and commas

I want to read the csv file as a pandas dataframe. CSV file is here: https://www.dropbox.com/s/o3xc74f8v4winaj/aaaa.csv?dl=0
In particular,
I want to skip the first row
The column headers are in row 2. In this case, they are: 1, 1, 2 and TOT. I do not want to hardcode them though. It is ok if the only column that gets extracted is TOT
I do not want to use a non-pandas approach if possible.
Here is what I am doing:
df = pandas.read_csv('https://www.dropbox.com/s/o3xc74f8v4winaj/aaaa.csv?dl=0', skiprows=1, skipinitialspace=True, sep=' ')
But this gives the error:
*** CParserError: Error tokenizing data. C error: Expected 5 fields in line 4, saw 6
The output should look something like this:
1 1 2 TOT
0 DEPTH(m) 0.01 1.24 1.52
1 BD 33kpa(t/m3) 1.6 1.6 1.6
2 SAND(%) 42.1 42.1 65.1
3 SILT(%) 37.9 37.9 16.9
4 CLAY(%) 20 20 18
5 ROCK(%) 12 12 12
6 WLS(kg/ha) 0 5 0.1 5.1
7 WLM(kg/ha) 0 5 0.1 5.1
8 WLSL(kg/ha) 0 4 0.1 4.1
9 WLSC(kg/ha) 0 2.1 0 2.1
10 WLMC(kg/ha) 0 2.1 0 2.1
11 WLSLC(kg/ha) 0 1.7 0 1.7
12 WLSLNC(kg/ha) 0 0.4 0 0.4
13 WBMC(kg/ha) 9 1102.1 250.9 1361.9
14 WHSC(kg/ha) 69 8432 1920 10420
15 WHPC(kg/ha) 146 18018 4102 22266
16 WOC(kg/ha) 224 27556 6272 34
17 WLSN(kg/ha) 0 0 0 0
18 WLMN(kg/ha) 0 0.2 0 0.2
19 WBMN(kg/ha) 0.9 110.2 25.1 136.2
20 WHSN(kg/ha) 7 843 192 1042
21 WHPN(kg/ha) 15 1802 410 2227
22 WON(kg/ha) 22 2755 627 3405
23 CFEM(kg/ha) 0

You can specify a regular expression to be used as your delimiter, in your case it will work with [\s,]{2,20}, i.e. 2 or more spaces or commas:
In [180]: pd.read_csv('aaaa.csv',
skiprows = 1,
sep='[\s,]{2,20}',
index_col=0)
Out[180]:
Unnamed: 1 1 1.1 2 TOT
0
1 DEPTH(m) 0.01 1.24 1.52 NaN
2 BD 33kpa(t/m3) 1.60 1.60 1.60 NaN
3 SAND(%) 42.10 42.10 65.10 NaN
4 SILT(%) 37.90 37.90 16.90 NaN
5 CLAY(%) 20.00 20.00 18.00 NaN
6 ROCK(%) 12.00 12.00 12.00 NaN
7 WLS(kg/ha) 0.00 5.00 0.10 5.1
8 WLM(kg/ha) 0.00 5.00 0.10 5.1
9 WLSL(kg/ha) 0.00 4.00 0.10 4.1
10 WLSC(kg/ha) 0.00 2.10 0.00 2.1
11 WLMC(kg/ha) 0.00 2.10 0.00 2.1
12 WLSLC(kg/ha) 0.00 1.70 0.00 1.7
13 WLSLNC(kg/ha) 0.00 0.40 0.00 0.4
14 WBMC(kg/ha) 9.00 1102.10 250.90 1361.9
15 WHSC(kg/ha) 69.00 8432.00 1920.00 10420.0
16 WHPC(kg/ha) 146.00 18018.00 4102.00 22266.0
17 WOC(kg/ha) 224.00 27556.00 6272.00 34.0
18 WLSN(kg/ha) 0.00 0.00 0.00 0.0
19 WLMN(kg/ha) 0.00 0.20 0.00 0.2
20 WBMN(kg/ha) 0.90 110.20 25.10 136.2
21 WHSN(kg/ha) 7.00 843.00 192.00 1042.0
22 WHPN(kg/ha) 15.00 1802.00 410.00 2227.0
23 WON(kg/ha) 22.00 2755.00 627.00 3405.0
24 CFEM(kg/ha) 0.00 NaN NaN NaN
25, None NaN NaN NaN NaN
26, None NaN NaN NaN NaN

You need to specify the names of the columns. Notice the trick I used to get two columns called 1 (one is an integer name and the other is text).
Given how badly the data is structured, this is not perfect (note row 2 where BD and 33kpa got split because of the space between them).
pd.read_csv('/Downloads/aaaa.csv',
skiprows=2,
skipinitialspace=True,
sep=' ',
names=['Index', 'Description',1,"1",2,'TOT'],
index_col=0)
Description 1 1 2 TOT
Index
1, DEPTH(m) 0.01 1.24 1.52 NaN
2, BD 33kpa(t/m3) 1.60 1.60 1.6
3, SAND(%) 42.1 42.10 65.10 NaN
4, SILT(%) 37.9 37.90 16.90 NaN
5, CLAY(%) 20.0 20.00 18.00 NaN
6, ROCK(%) 12.0 12.00 12.00 NaN
7, WLS(kg/ha) 0.0 5.00 0.10 5.1
8, WLM(kg/ha) 0.0 5.00 0.10 5.1
9, WLSL(kg/ha) 0.0 4.00 0.10 4.1
10, WLSC(kg/ha) 0.0 2.10 0.00 2.1
11, WLMC(kg/ha) 0.0 2.10 0.00 2.1
12, WLSLC(kg/ha) 0.0 1.70 0.00 1.7
13, WLSLNC(kg/ha) 0.0 0.40 0.00 0.4
14, WBMC(kg/ha) 9.0 1102.10 250.90 1361.9
15, WHSC(kg/ha) 69. 8432.00 1920.00 10420.0
16, WHPC(kg/ha) 146. 18018.00 4102.00 22266.0
17, WOC(kg/ha) 224. 27556.00 6272.00 34.0
18, WLSN(kg/ha) 0.0 0.00 0.00 0.0
19, WLMN(kg/ha) 0.0 0.20 0.00 0.2
20, WBMN(kg/ha) 0.9 110.20 25.10 136.2
21, WHSN(kg/ha) 7. 843.00 192.00 1042.0
22, WHPN(kg/ha) 15. 1802.00 410.00 2227.0
23, WON(kg/ha) 22. 2755.00 627.00 3405.0
24, CFEM(kg/ha) 0. NaN NaN NaN
25, NaN NaN NaN NaN NaN
26, NaN NaN NaN NaN NaN
Or you can reset the index.
>>> (pd.read_csv('/Downloads/aaaa.csv',
skiprows=2,
skipinitialspace=True,
sep=' ',
names=['Index', 'Description',1,"1",2,'TOT'],
index_col=0)
.reset_index(drop=True)
.dropna(axis=0, how='all'))
Description 1 1 2 TOT
0 DEPTH(m) 0.01 1.24 1.52 NaN
1 BD 33kpa(t/m3) 1.60 1.60 1.6
2 SAND(%) 42.1 42.10 65.10 NaN
3 SILT(%) 37.9 37.90 16.90 NaN
4 CLAY(%) 20.0 20.00 18.00 NaN
5 ROCK(%) 12.0 12.00 12.00 NaN
6 WLS(kg/ha) 0.0 5.00 0.10 5.1
7 WLM(kg/ha) 0.0 5.00 0.10 5.1
8 WLSL(kg/ha) 0.0 4.00 0.10 4.1
9 WLSC(kg/ha) 0.0 2.10 0.00 2.1
10 WLMC(kg/ha) 0.0 2.10 0.00 2.1
11 WLSLC(kg/ha) 0.0 1.70 0.00 1.7
12 WLSLNC(kg/ha) 0.0 0.40 0.00 0.4
13 WBMC(kg/ha) 9.0 1102.10 250.90 1361.9
14 WHSC(kg/ha) 69. 8432.00 1920.00 10420.0
15 WHPC(kg/ha) 146. 18018.00 4102.00 22266.0
16 WOC(kg/ha) 224. 27556.00 6272.00 34.0
17 WLSN(kg/ha) 0.0 0.00 0.00 0.0
18 WLMN(kg/ha) 0.0 0.20 0.00 0.2
19 WBMN(kg/ha) 0.9 110.20 25.10 136.2
20 WHSN(kg/ha) 7. 843.00 192.00 1042.0
21 WHPN(kg/ha) 15. 1802.00 410.00 2227.0
22 WON(kg/ha) 22. 2755.00 627.00 3405.0
23 CFEM(kg/ha) 0. NaN NaN NaN

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