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In a dataframe, with some empty(NaN) values in some rows - Example below
s = pd.DataFrame([[39877380,158232151,20], [39877380,332086469,], [39877380,39877381,14], [39877380,39877383,8], [73516838,6439138,1], [73516838,6500551,], [735571896,203559638,], [735571896,282186552,], [736453090,6126187,], [673117474,12196071,], [673117474,12209800,], [673117474,618058747,6]], columns=['start','end','total'])
When I groupby start and end columns
s.groupby(['start', 'end']).total.sum()
the output I get is
start end
39877380 39877381 14.00
39877383 8.00
158232151 20.00
332086469 nan
73516838 6439138 1.00
6500551 nan
673117474 12196071 nan
12209800 nan
618058747 6.00
735571896 203559638 nan
282186552 nan
736453090 6126187 nan
I want to exclude all the groups of start where all values with end is 'nan' - Expected output -
start end
39877380 39877381 14.00
39877383 8.00
158232151 20.00
332086469 nan
73516838 6439138 1.00
6500551 nan
673117474 12196071 nan
12209800 nan
618058747 6.00
I tried with dropna(), but it is removing all the nan values and not nan groups.
I am newbie in python and pandas. Can someone help me in this? thank you
In newer pandas versions is necessary use min_count=1 for missing values if use sum:
s1 = s.groupby(['start', 'end']).total.sum(min_count=1)
#oldier pandas version solution
#s1 = s.groupby(['start', 'end']).total.sum()
Then is possible filter if at least one non missing value per first level by Series.notna with GroupBy.transform and GroupBy.any, filtering is by boolean indexing:
s2 = s1[s1.notna().groupby(level=0).transform('any')]
#oldier pandas version solution
#s2 = s1[s1.notnull().groupby(level=0).transform('any')]
print (s2)
start end
39877380 39877381 14.0
39877383 8.0
158232151 20.0
332086469 NaN
73516838 6439138 1.0
6500551 NaN
673117474 12196071 NaN
12209800 NaN
618058747 6.0
Name: total, dtype: float64
Or is possible get unique values of first level index values by MultiIndex.get_level_values and filtering by DataFrame.loc:
idx = s1.index.get_level_values(0)
s2 = s1.loc[idx[s1.notna()].unique()]
#oldier pandas version solution
#s2 = s1.loc[idx[s1.notnull()].unique()]
print (s2)
start end
39877380 39877381 14.0
39877383 8.0
158232151 20.0
332086469 NaN
73516838 6439138 1.0
6500551 NaN
673117474 12196071 NaN
12209800 NaN
618058747 6.0
Name: total, dtype: float64
Currently i'm working on a Livetiming-Software for a motorsport-application. Therefore i have to crawl a Livetiming-Webpage and copy the Data to a big Dataframe. This Dataframe is the source of several diagramms i want to make. To keep my Dataframe up to date, i have to crawl the webpage very often.
I can download the Data and save them as a Panda.Dataframe. But my Problem is step from the downloaded DataFrame to the Big Dataframe, that includes all the Data.
import pandas as pd
import numpy as np
df1= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':['1:30,000','1:45,000','1:50,000','1:25,333','1:13,366','1:17,000'],
'Laps':['1','1','1','1','1','1']})
df2= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,],
'Laps':['2','2','2','2','2','2']})
df3= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':['1:31,000','1:41,000','1:51,000','1:21,333','1:11,366','1:11,000'],
'Laps':['2','2','2','2','2','2']})
df1.set_index(['CLS','Nr.','Laps'],inplace=True)
df2.set_index(['CLS','Nr.','Laps'],inplace=True)
df3.set_index(['CLS','Nr.','Laps'],inplace=True)
df1 shows a Dataframe from previous laps.
df2 shows a Dataframe in the second lap. The Lap is not completed, so i have a nan.
df3 shows a Dataframe after the second lap is completed.
My target is to have just one row for each Lap per Car per Class.
Either i have the problem, that i have duplicates with incomplete Laps or all date get overwritten.
I hope that someone can help me with this problem.
Thank you so far.
MrCrunsh
If I understand your problem correctly, your issue is that you have overlapping data for the second lap: information while the lap is still in progress and information after it's over. If you want to put all the information for a given lap in one row, I'd suggest use multi-index columns or changing the column names to reflect the difference between measurements during and after laps.
df = pd.concat([df1, df3])
df = pd.concat([df, df2], axis=1, keys=['after', 'during'])
The result will look like this:
after during
Pos Zeit Pos Zeit
CLS Nr. Laps
V4 24 1 5 1:13,366 NaN NaN
2 5 1:11,366 5.0 NaN
55 1 4 1:25,333 NaN NaN
2 4 1:21,333 4.0 NaN
985 1 6 1:17,000 NaN NaN
2 6 1:11,000 6.0 NaN
V5 13 1 1 1:30,000 NaN NaN
2 1 1:31,000 1.0 NaN
30 1 3 1:50,000 NaN NaN
2 3 1:51,000 3.0 NaN
700 1 2 1:45,000 NaN NaN
2 2 1:41,000 2.0 NaN
I'm trying to get data from txt file with pandas.read_csv but it doesn't show the repeated(same) values in the file such as I have 2043 in the row but It shows it once not in every row.
My file sample
Result set
All the circles I've drawn should be 2043 also but they are empty.
My code is :
import pandas as pd
df= pd.read_csv('samplefile.txt', sep='\t', header=None,
names = ["234", "235", "236"]
You get MultiIndex, so first level value are not shown only.
You can convert MultiIndex to columns by reset_index:
df = df.reset_index()
Or specify each column in parameter names for avoid MultiIndex:
df = pd.read_csv('samplefile.txt', sep='\t', names = ["one","two","next", "234", "235", "236"]
A word of warning with MultiIndex as I was bitten by this yesterday and wasted time trying to trouble shoot a non-existant problem.
If one of your index levels is of type float64 then you may find that the indexes are not shown in full. I had a dataframe I was df.groupby().describe() and the variable I was performing the groupby() on was originally a long int, at some point it was converted to a float and when printing out this index was rounded. There were a number of values very close to each other and so it appeared on printing that the groupby() had found multiple levels of the second index.
Thats not very clear so here is an illustrative example...
import numpy as np
import pandas as pd
index = np.random.uniform(low=89908893132829,
high=89908893132929,
size=(50,))
df = pd.DataFrame({'obs': np.arange(100)},
index=np.append(index, index)).sort_index()
df.index.name = 'index1'
df['index2'] = [1, 2] * 50
df.reset_index(inplace=True)
df.set_index(['index1', 'index2'], inplace=True)
Look at the dataframe and it appears that there is only one level of index1...
df.head(10)
obs
index1 index2
8.990889e+13 1 4
2 54
1 61
2 11
1 89
2 39
1 65
2 15
1 60
2 10
groupby(['index1', 'index2']).describe() and it looks like there is only one level of index1...
summary = df.groupby(['index1', 'index2']).describe()
summary.head()
obs
count mean std min 25% 50% 75% max
index1 index2
8.990889e+13 1 1.0 4.0 NaN 4.0 4.0 4.0 4.0 4.0
2 1.0 54.0 NaN 54.0 54.0 54.0 54.0 54.0
1 1.0 61.0 NaN 61.0 61.0 61.0 61.0 61.0
2 1.0 11.0 NaN 11.0 11.0 11.0 11.0 11.0
1 1.0 89.0 NaN 89.0 89.0 89.0 89.0 89.0
But if you look at the actual values of index1 in either you see that there are multiple unique values. In the original dataframe...
df.index.get_level_values('index1')
Float64Index([89908893132833.12, 89908893132833.12, 89908893132834.08,
89908893132834.08, 89908893132835.05, 89908893132835.05,
89908893132836.3, 89908893132836.3, 89908893132837.95,
89908893132837.95, 89908893132838.1, 89908893132838.1,
89908893132838.6, 89908893132838.6, 89908893132841.89,
89908893132841.89, 89908893132841.95, 89908893132841.95,
89908893132845.81, 89908893132845.81, 89908893132845.83,
89908893132845.83, 89908893132845.88, 89908893132845.88,
89908893132846.02, 89908893132846.02, 89908893132847.2,
89908893132847.2, 89908893132847.67, 89908893132847.67,
89908893132848.5, 89908893132848.5, 89908893132848.5,
89908893132848.5, 89908893132855.17, 89908893132855.17,
89908893132855.45, 89908893132855.45, 89908893132864.62,
89908893132864.62, 89908893132868.61, 89908893132868.61,
89908893132873.16, 89908893132873.16, 89908893132875.6,
89908893132875.6, 89908893132875.83, 89908893132875.83,
89908893132878.73, 89908893132878.73, 89908893132879.9,
89908893132879.9, 89908893132880.67, 89908893132880.67,
89908893132880.69, 89908893132880.69, 89908893132881.31,
89908893132881.31, 89908893132881.69, 89908893132881.69,
89908893132884.45, 89908893132884.45, 89908893132887.27,
89908893132887.27, 89908893132887.83, 89908893132887.83,
89908893132892.8, 89908893132892.8, 89908893132894.34,
89908893132894.34, 89908893132894.5, 89908893132894.5,
89908893132901.88, 89908893132901.88, 89908893132903.27,
89908893132903.27, 89908893132904.53, 89908893132904.53,
89908893132909.27, 89908893132909.27, 89908893132910.38,
89908893132910.38, 89908893132911.86, 89908893132911.86,
89908893132913.4, 89908893132913.4, 89908893132915.73,
89908893132915.73, 89908893132916.06, 89908893132916.06,
89908893132922.48, 89908893132922.48, 89908893132923.44,
89908893132923.44, 89908893132924.66, 89908893132924.66,
89908893132925.14, 89908893132925.14, 89908893132928.28,
89908893132928.28],
dtype='float64', name='index1')
...and in the summarised dataframe...
summary.index.get_level_values('index1')
Float64Index([89908893132833.12, 89908893132833.12, 89908893132834.08,
89908893132834.08, 89908893132835.05, 89908893132835.05,
89908893132836.3, 89908893132836.3, 89908893132837.95,
89908893132837.95, 89908893132838.1, 89908893132838.1,
89908893132838.6, 89908893132838.6, 89908893132841.89,
89908893132841.89, 89908893132841.95, 89908893132841.95,
89908893132845.81, 89908893132845.81, 89908893132845.83,
89908893132845.83, 89908893132845.88, 89908893132845.88,
89908893132846.02, 89908893132846.02, 89908893132847.2,
89908893132847.2, 89908893132847.67, 89908893132847.67,
89908893132848.5, 89908893132848.5, 89908893132855.17,
89908893132855.17, 89908893132855.45, 89908893132855.45,
89908893132864.62, 89908893132864.62, 89908893132868.61,
89908893132868.61, 89908893132873.16, 89908893132873.16,
89908893132875.6, 89908893132875.6, 89908893132875.83,
89908893132875.83, 89908893132878.73, 89908893132878.73,
89908893132879.9, 89908893132879.9, 89908893132880.67,
89908893132880.67, 89908893132880.69, 89908893132880.69,
89908893132881.31, 89908893132881.31, 89908893132881.69,
89908893132881.69, 89908893132884.45, 89908893132884.45,
89908893132887.27, 89908893132887.27, 89908893132887.83,
89908893132887.83, 89908893132892.8, 89908893132892.8,
89908893132894.34, 89908893132894.34, 89908893132894.5,
89908893132894.5, 89908893132901.88, 89908893132901.88,
89908893132903.27, 89908893132903.27, 89908893132904.53,
89908893132904.53, 89908893132909.27, 89908893132909.27,
89908893132910.38, 89908893132910.38, 89908893132911.86,
89908893132911.86, 89908893132913.4, 89908893132913.4,
89908893132915.73, 89908893132915.73, 89908893132916.06,
89908893132916.06, 89908893132922.48, 89908893132922.48,
89908893132923.44, 89908893132923.44, 89908893132924.66,
89908893132924.66, 89908893132925.14, 89908893132925.14,
89908893132928.28, 89908893132928.28],
dtype='float64', name='index1')
I wasted time scratching my head wondering why my groupby([index1,index2) had produced only one level of index1!
I have a pandas Dataframe containing EOD financial data (OHLC) for analysis.
I'm using https://github.com/cirla/tulipy library to generate technical indicator values, that have a certain timeperiod as option. For Example. ADX with timeperiod=5 shows ADX for last 5 days.
Because of this timeperiod, the generated array with indicator values is always shorter in length than the Dataframe. Because the prices of first 5 days are used to generate ADX for day 6..
pdi14, mdi14 = ti.di(
high=highData, low=lowData, close=closeData, period=14)
df['mdi_14'] = mdi14
df['pdi_14'] = pdi14
>> ValueError: Length of values does not match length of index
Unfortunately, unlike TA-LIB for example, this tulip library does not provide NaN-values for these first couple of empty days...
Is there an easy way to prepend these NaN to the ndarray?
Or insert into df at a certain index & have it create NaN for the rows before it automatically?
Thanks in advance, I've been researching for days!
Maybe make the shift yourself in the code ?
period = 14
pdi14, mdi14 = ti.di(
high=highData, low=lowData, close=closeData, period=period
)
df['mdi_14'] = np.NAN
df['mdi_14'][period - 1:] = mdi14
I hope they will fill the first values with NAN in the lib in the future. It's dangerous to leave time series data like this without any label.
Full MCVE
df = pd.DataFrame(1, range(10), list('ABC'))
a = np.full((len(df) - 6, df.shape[1]), 2)
b = np.full((6, df.shape[1]), np.nan)
c = np.row_stack([b, a])
d = pd.DataFrame(c, df.index, df.columns)
d
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 2.0 2.0 2.0
7 2.0 2.0 2.0
8 2.0 2.0 2.0
9 2.0 2.0 2.0
The C version of the tulip library includes a start function for each indicator (reference: https://tulipindicators.org/usage) that can be used to determine the output length of an indicator given a set of input options. Unfortunately, it does not appear that the python bindings library, tulipy, includes this functionality. Instead you have to resort to dynamically reassigning your index values to align the output with the original DataFrame.
Here is an example that uses the price series from the tulipy docs:
#Create the dataframe with close prices
prices = pd.DataFrame(data={81.59, 81.06, 82.87, 83, 83.61, 83.15, 82.84, 83.99, 84.55,
84.36, 85.53, 86.54, 86.89, 87.77, 87.29}, columns=['close'])
#Compute the technical indicator using tulipy and save the result in a DataFrame
bbands = pd.DataFrame(data=np.transpose(ti.bbands(real = prices['close'].to_numpy(), period = 5, stddev = 2)))
#Dynamically realign the index; note from the tulip library documentation that the price/volume data is expected be ordered "oldest to newest (index 0 is oldest)"
bbands.index += prices.index.max() - bbands.index.max()
#Put the indicator values with the original DataFrame
prices[['BBANDS_5_2_low', 'BBANDS_5_2_mid', 'BBANDS_5_2_up']] = bbands
prices.head(15)
close BBANDS_5_2_low BBANDS_5_2_mid BBANDS_5_2_up
0 81.06 NaN NaN NaN
1 81.59 NaN NaN NaN
2 82.87 NaN NaN NaN
3 83.00 NaN NaN NaN
4 83.61 80.530042 82.426 84.321958
5 83.15 81.494061 82.844 84.193939
6 82.84 82.533343 83.094 83.654657
7 83.99 82.471983 83.318 84.164017
8 84.55 82.417750 83.628 84.838250
9 84.36 82.435203 83.778 85.120797
10 85.53 82.511331 84.254 85.996669
11 86.54 83.142618 84.994 86.845382
12 86.89 83.536488 85.574 87.611512
13 87.77 83.870324 86.218 88.565676
14 87.29 85.288871 86.804 88.319129
I am having two data frame as given below
df1=
2492 3853 2486 3712 2288
0 4 NaN 3.5 NaN NaN
1 3 NaN 2.0 4.5 3.5
2 3 3.5 4.5 NaN 3.5
3 3. NaN 3.5 4.5 NaN
df2=
2492 0.476683
3853 0.464110
2486 0.438992
3712 0.400275
2288 0.379856
Right now I would like to get the sum of df2 values by excluding the NaN Values
Expected output
0 0.915675[0.476683+0.438992]
1 1.695806[0.476683+0.438992+0.400275+0.379856]
2 1.759641[0.476683+0.464110+0.438992+0.379856]
3 1.31595 [0.476683+0.438992+0.400275]
Please let me know your thoughts how to achieve this issue(without replacing NaN values as "0" )
df2.sum(1).sum()
Should be enough and skip NaNs.
The first sum is a DataFrame method that returns a Series which contains the sum for every line, then the second is summing the values on this Series.
NaNs are ignored by default.
edit: using simply df2.sum() should be enough
You can do:
>>> ((df1.fillna(0)>0)*1).mul(df2.iloc[:,1].values).sum(axis=1)
0 0.915675
1 1.695806
2 1.759641
3 1.315950
dtype: float64
Note that NaN are not replaced "by reference", you still have NaN in your original df1 after this operation.