I have a sample dataframe as given below.
import pandas as pd
import numpy as np
NaN = np.nan
data = {'ID':['A', 'A', 'A', 'B','B','B'],
'Date':['2021-09-20 04:34:57', '2021-09-20 04:37:25', '2021-09-20 04:38:26', '2021-09-01
00:12:29','2021-09-01 11:20:58','2021-09-02 09:20:58'],
'Name':['xx','xx',NaN,'yy',NaN,NaN],
'Height':[174,174,NaN,160,NaN,NaN],
'Weight':[74,NaN,NaN,58,NaN,NaN],
'Gender':[NaN,'Male',NaN,NaN,'Female',NaN],
'Interests':[NaN,NaN,'Hiking,Sports',NaN,NaN,'Singing']}
df1 = pd.DataFrame(data)
df1
I want to combine the data present on the same date into a single row. The 'Date' column is in timestamp format. I have written a code for it. Here is my TRY code:
TRY:
df1['Date'] = pd.to_datetime(df1['Date'])
df_out = (df1.groupby(['ID', pd.Grouper(key='Date', freq='D')])
.agg(lambda x: ''.join(x.dropna().astype(str)))
.reset_index()
).replace('', np.nan)
This gives an output where if there are multiple entries of same value, the final result has multiple entries in the same row as shown below.
Obtained Output
However, I do not want the values to be repeated if there are multiple entries. The final output should look like the image shown below.
Required Output
The first column should not have 'xx' and 174.0 instead of 'xxxx' and '174.0 174.0'.
Any help is greatly appreciated. Thank you.
In your case replace agg join to first
df_out = (df1.groupby(['ID', pd.Grouper(key='Date', freq='D')])
.first()
.reset_index()
).replace('', np.nan)
df_out
Out[113]:
ID Date Name Height Weight Gender Interests
0 A 2021-09-20 xx 174.0 74.0 Male Hiking,Sports
1 B 2021-09-01 yy 160.0 58.0 Female None
2 B 2021-09-02 None NaN NaN None Singing
Since you're only trying to keep the first available value for each column for each date, you can do:
>>> df1.groupby(["ID", pd.Grouper(key='Date', freq='D')]).agg("first").reset_index()
ID Date Name Height Weight Gender Interests
0 A 2021-09-20 xx 174.0 74.0 Male Hiking,Sports
1 B 2021-09-01 yy 160.0 58.0 Female None
2 B 2021-09-02 None NaN NaN None Singing
From
Fill in missing row values in pandas dataframe
I have the following dataframe and would like to fill in missing values.
mukey hzdept_r hzdepb_r sandtotal_r silttotal_r
425897 0 61
425897 61 152 5.3 44.7
425911 0 30 30.1 54.9
425911 30 74 17.7 49.8
425911 74 84
I want each missing value to be the average of values corresponding to that mukey. In this case, e.g. the first row missing values will be the average of sandtotal_r and silttotal_r corresponding to mukey==425897. pandas fillna doesn't seem to do the trick. Any help?
While the code works for the sample dataframe in that example, it is failing on the larger dataset I have uploaded here: https://www.dropbox.com/s/w3m0jppnq74op4c/www004.csv?dl=0
import pandas as pd
df = pd.read_csv('www004.csv')
# CSV file is here: https://www.dropbox.com/s/w3m0jppnq74op4c/www004.csv?dl=0
df1 = df.set_index('mukey')
df1.fillna(df.groupby('mukey').mean(),inplace=True)
df1.reset_index()
I get the error: InvalidIndexError. Why is it not working?
Use combine_first. It allows you to patch up the missing data on the left dataframe with the matching data on the right dataframe based on same index.
In this case, df1 is on the left and df2, the means, as the one on the right.
In [48]: df = pd.read_csv('www004.csv')
...: df1 = df.set_index('mukey')
...: df2 = df.groupby('mukey').mean()
In [49]: df1.loc[426178,:]
Out[49]:
hzdept_r hzdepb_r sandtotal_r silttotal_r claytotal_r om_r
mukey
426178 0 36 NaN NaN NaN 72.50
426178 36 66 NaN NaN NaN 72.50
426178 66 152 42.1 37.9 20 0.25
In [50]: df2.loc[426178,:]
Out[50]:
hzdept_r 34.000000
hzdepb_r 84.666667
sandtotal_r 42.100000
silttotal_r 37.900000
claytotal_r 20.000000
om_r 48.416667
Name: 426178, dtype: float64
In [51]: df3 = df1.combine_first(df2)
...: df3.loc[426178,:]
Out[51]:
hzdept_r hzdepb_r sandtotal_r silttotal_r claytotal_r om_r
mukey
426178 0 36 42.1 37.9 20 72.50
426178 36 66 42.1 37.9 20 72.50
426178 66 152 42.1 37.9 20 0.25
Note that the following rows still won't have values in the resulting df3
426162
426163
426174
426174
426255
because they were single rows to begin with, hence, .mean() doesn't mean anything to them (eh, see what I did there?).
The problem is the duplicate index values. When you use df1.fillna(df2), if you have multiple NaN entries in df1 where both the index and the column label are the same, pandas will get confused when trying to slice df1, and throw that InvalidIndexError.
Your sample dataframe works because even though you have duplicate index values there, only one of each index value is null. Your larger dataframe contains null entries that share both the index value and column label in some cases.
To make this work, you can do this one column at a time. For some reason, when operating on a series, pandas will not get confused by multiple entries of the same index, and will simply fill the same value in each one. Hence, this should work:
import pandas as pd
df = pd.read_csv('www004.csv')
# CSV file is here: https://www.dropbox.com/s/w3m0jppnq74op4c/www004.csv?dl=0
df1 = df.set_index('mukey')
grouped = df.groupby('mukey').mean()
for col in ['sandtotal_r', 'silttotal_r']:
df1[col] = df1[col].fillna(grouped[col])
df1.reset_index()
NOTE: Be careful using the combine_first method if you ever have "extra" data in the dataframe you're filling from. The combine_first function will include ALL indices from the dataframe you're filling from, even if they're not present in the original dataframe.
I am getting a ValueError: cannot reindex from a duplicate axis when I am trying to set an index to a certain value. I tried to reproduce this with a simple example, but I could not do it.
Here is my session inside of ipdb trace. I have a DataFrame with string index, and integer columns, float values. However when I try to create sum index for sum of all columns I am getting ValueError: cannot reindex from a duplicate axis error. I created a small DataFrame with the same characteristics, but was not able to reproduce the problem, what could I be missing?
I don't really understand what ValueError: cannot reindex from a duplicate axismeans, what does this error message mean? Maybe this will help me diagnose the problem, and this is most answerable part of my question.
ipdb> type(affinity_matrix)
<class 'pandas.core.frame.DataFrame'>
ipdb> affinity_matrix.shape
(333, 10)
ipdb> affinity_matrix.columns
Int64Index([9315684, 9315597, 9316591, 9320520, 9321163, 9320615, 9321187, 9319487, 9319467, 9320484], dtype='int64')
ipdb> affinity_matrix.index
Index([u'001', u'002', u'003', u'004', u'005', u'008', u'009', u'010', u'011', u'014', u'015', u'016', u'018', u'020', u'021', u'022', u'024', u'025', u'026', u'027', u'028', u'029', u'030', u'032', u'033', u'034', u'035', u'036', u'039', u'040', u'041', u'042', u'043', u'044', u'045', u'047', u'047', u'048', u'050', u'053', u'054', u'055', u'056', u'057', u'058', u'059', u'060', u'061', u'062', u'063', u'065', u'067', u'068', u'069', u'070', u'071', u'072', u'073', u'074', u'075', u'076', u'077', u'078', u'080', u'082', u'083', u'084', u'085', u'086', u'089', u'090', u'091', u'092', u'093', u'094', u'095', u'096', u'097', u'098', u'100', u'101', u'103', u'104', u'105', u'106', u'107', u'108', u'109', u'110', u'111', u'112', u'113', u'114', u'115', u'116', u'117', u'118', u'119', u'121', u'122', ...], dtype='object')
ipdb> affinity_matrix.values.dtype
dtype('float64')
ipdb> 'sums' in affinity_matrix.index
False
Here is the error:
ipdb> affinity_matrix.loc['sums'] = affinity_matrix.sum(axis=0)
*** ValueError: cannot reindex from a duplicate axis
I tried to reproduce this with a simple example, but I failed
In [32]: import pandas as pd
In [33]: import numpy as np
In [34]: a = np.arange(35).reshape(5,7)
In [35]: df = pd.DataFrame(a, ['x', 'y', 'u', 'z', 'w'], range(10, 17))
In [36]: df.values.dtype
Out[36]: dtype('int64')
In [37]: df.loc['sums'] = df.sum(axis=0)
In [38]: df
Out[38]:
10 11 12 13 14 15 16
x 0 1 2 3 4 5 6
y 7 8 9 10 11 12 13
u 14 15 16 17 18 19 20
z 21 22 23 24 25 26 27
w 28 29 30 31 32 33 34
sums 70 75 80 85 90 95 100
This error usually rises when you join / assign to a column when the index has duplicate values. Since you are assigning to a row, I suspect that there is a duplicate value in affinity_matrix.columns, perhaps not shown in your question.
As others have said, you've probably got duplicate values in your original index. To find them do this:
df[df.index.duplicated()]
Indices with duplicate values often arise if you create a DataFrame by concatenating other DataFrames. IF you don't care about preserving the values of your index, and you want them to be unique values, when you concatenate the the data, set ignore_index=True.
Alternatively, to overwrite your current index with a new one, instead of using df.reindex(), set:
df.index = new_index
Simple Fix
Run this before grouping
df = df.reset_index()
Thanks to this github comment for the solution.
For people who are still struggling with this error, it can also happen if you accidentally create a duplicate column with the same name. Remove duplicate columns like so:
df = df.loc[:,~df.columns.duplicated()]
Simply skip the error using .values at the end.
affinity_matrix.loc['sums'] = affinity_matrix.sum(axis=0).values
I came across this error today when I wanted to add a new column like this
df_temp['REMARK_TYPE'] = df.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)
I wanted to process the REMARK column of df_temp to return 1 or 0. However I typed wrong variable with df. And it returned error like this:
----> 1 df_temp['REMARK_TYPE'] = df.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
2417 else:
2418 # set column
-> 2419 self._set_item(key, value)
2420
2421 def _setitem_slice(self, key, value):
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
2483
2484 self._ensure_valid_index(value)
-> 2485 value = self._sanitize_column(key, value)
2486 NDFrame._set_item(self, key, value)
2487
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in _sanitize_column(self, key, value, broadcast)
2633
2634 if isinstance(value, Series):
-> 2635 value = reindexer(value)
2636
2637 elif isinstance(value, DataFrame):
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in reindexer(value)
2625 # duplicate axis
2626 if not value.index.is_unique:
-> 2627 raise e
2628
2629 # other
ValueError: cannot reindex from a duplicate axis
As you can see it, the right code should be
df_temp['REMARK_TYPE'] = df_temp.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)
Because df and df_temp have a different number of rows. So it returned ValueError: cannot reindex from a duplicate axis.
Hope you can understand it and my answer can help other people to debug their code.
In my case, this error popped up not because of duplicate values, but because I attempted to join a shorter Series to a Dataframe: both had the same index, but the Series had fewer rows (missing the top few). The following worked for my purposes:
df.head()
SensA
date
2018-04-03 13:54:47.274 -0.45
2018-04-03 13:55:46.484 -0.42
2018-04-03 13:56:56.235 -0.37
2018-04-03 13:57:57.207 -0.34
2018-04-03 13:59:34.636 -0.33
series.head()
date
2018-04-03 14:09:36.577 62.2
2018-04-03 14:10:28.138 63.5
2018-04-03 14:11:27.400 63.1
2018-04-03 14:12:39.623 62.6
2018-04-03 14:13:27.310 62.5
Name: SensA_rrT, dtype: float64
df = series.to_frame().combine_first(df)
df.head(10)
SensA SensA_rrT
date
2018-04-03 13:54:47.274 -0.45 NaN
2018-04-03 13:55:46.484 -0.42 NaN
2018-04-03 13:56:56.235 -0.37 NaN
2018-04-03 13:57:57.207 -0.34 NaN
2018-04-03 13:59:34.636 -0.33 NaN
2018-04-03 14:00:34.565 -0.33 NaN
2018-04-03 14:01:19.994 -0.37 NaN
2018-04-03 14:02:29.636 -0.34 NaN
2018-04-03 14:03:31.599 -0.32 NaN
2018-04-03 14:04:30.779 -0.33 NaN
2018-04-03 14:05:31.733 -0.35 NaN
2018-04-03 14:06:33.290 -0.38 NaN
2018-04-03 14:07:37.459 -0.39 NaN
2018-04-03 14:08:36.361 -0.36 NaN
2018-04-03 14:09:36.577 -0.37 62.2
I wasted couple of hours on the same issue. In my case, I had to reset_index() of a dataframe before using apply function.
Before merging, or looking up from another indexed dataset, you need to reset the index as 1 dataset can have only 1 Index.
I got this error when I tried adding a column from a different table. Indeed I got duplicate index values along the way. But it turned out I was just doing it wrong: I actually needed to df.join the other table.
This pointer might help someone in a similar situation.
In my case it was caused by mismatch in dimensions:
accidentally using a column from different df during the mul operation
This can also be a cause for this[:) I solved my problem like this]
It may happen even if you are trying to insert a dataframe type column inside dataframe
you can try this
df['my_new']=pd.Series(my_new.values)
if you get this error after merging two dataframe and remove suffix adnd try to write to excel
Your problem is that there are columns you are not merging on that are common to both source DataFrames. Pandas needs a way to say which one came from where, so it adds the suffixes, the defaults being '_x' on the left and '_y' on the right.
If you have a preference on which source data frame to keep the columns from, then you can set the suffixes and filter accordingly, for example if you want to keep the clashing columns from the left:
# Label the two sides, with no suffix on the side you want to keep
df = pd.merge(
df,
tempdf[what_i_care_about],
on=['myid', 'myorder'],
how='outer',
suffixes=('', '_delete_suffix') # Left gets no suffix, right gets something identifiable
)
# Discard the columns that acquired a suffix
df = df[[c for c in df.columns if not c.endswith('_delete_suffix')]]
Alternatively, you can drop one of each of the clashing columns prior to merging, then Pandas has no need to assign a suffix.
Just add .to_numpy() to the end of the series you want to concatenate.
It happened to me when I appended 2 dataframes into another (df3 = df1.append(df2)), so the output was:
df1
A B
0 1 a
1 2 b
2 3 c
df2
A B
0 4 d
1 5 e
2 6 f
df3
A B
0 1 a
1 2 b
2 3 c
0 4 d
1 5 e
2 6 f
The simplest way to fix the indexes is using the "df.reset_index(drop=bool, inplace=bool)" method, as Connor said... you can also set the 'drop' argument True to avoid the index list to be created as a columns, and 'inplace' to True to make the indexes reset permanent.
Here is the official refference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reset_index.html
In addition, you can also use the ".set_index(keys=list, inplace=bool)" method, like this:
new_index_list = list(range(0, len(df3)))
df3['new_index'] = new_index_list
df3.set_index(keys='new_index', inplace=True)
official refference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_index.html
Make sure your index does not have any duplicates, I simply did df.reset_index(drop=True, inplace=True) and I don't get the error anymore! But you might want to keep the index, in that case just set drop to False
df = df.reset_index(drop=True) worked for me
I was trying to create a histogram using seaborn.
sns.histplot(data=df, x='Blood Chemistry 1', hue='Outcome', discrete=False, multiple='stack')
I get ValueError: cannot reindex from a duplicate axis. To solve it, I had to choose only the rows where x has no missing values:
data = df[~df['Blood Chemistry 1'].isnull()]
I am working on several taxi datasets. I have used pandas to concat all the dataset into a single dataframe.
My dataframe looks something like this.
675 1039 #and rest 125 taxis
longitude latitude longitude latitude
date
2008-02-02 13:31:21 116.56359 40.06489 Nan Nan
2008-02-02 13:31:51 116.56486 40.06415 Nan Nan
2008-02-02 13:32:21 116.56855 40.06352 116.58243 39.6313
2008-02-02 13:32:51 116.57127 40.06324 Nan Nan
2008-02-02 13:33:21 116.57120 40.06328 116.55134 39.6313
2008-02-02 13:33:51 116.57121 40.06329 116.55126 39.6123
2008-02-02 13:34:21 Nan Nan 116.55134 39.5123
where 675,1039 are the taxi ids. Basically there are totally 127 taxis having their corresponding latitudes and longitudes columned up.
I have several ways to extract not-null values for a row.
df.ix[k,df.columns[np.isnan(df.irow(0))!=1]]
(or)
df.irow(0)[np.isnan(df.irow(0))!=1]
(or)
df.irow(0)[np.where(df.irow(0)[df.columns].notnull())[0]]
any of the above commands will return,
675 longitude 116.56359
latitude 40.064890
4549 longitude 116.34642
latitude 39.96662
Name: 2008-02-02 13:31:21
now i want to extract all the notnull values from first few rows(say from row 1 to row 6).
how do i do that?
i can probably loop it up. But i want a non-looped way of doing it.
Any help, suggestions are welcome.
Thanks in adv! :)
df.ix[1:6].dropna(axis=1)
As a heads up, irow will be deprecated in the next release of pandas. New methods, with clearer usage, replace it.
http://pandas.pydata.org/pandas-docs/dev/indexing.html#deprecations
In 0.11 (0.11rc1 is out now), this is very easy using .iloc to first select the first 6 rows, then dropna drops any row with a nan (you can also pass some options to dropna to control exactly which columns you want considered)
I realized you want 1:6, I did 0:6 in my answer....
In [8]: df = DataFrame(randn(10,3),columns=list('ABC'),index=date_range('20130101',periods=10))
In [9]: df.ix[6,'A'] = np.nan
In [10]: df.ix[6,'B'] = np.nan
In [11]: df.ix[2,'A'] = np.nan
In [12]: df.ix[4,'B'] = np.nan
In [13]: df.iloc[0:6]
Out[13]:
A B C
2013-01-01 0.442692 -0.109415 -0.038182
2013-01-02 1.217950 0.006681 -0.067752
2013-01-03 NaN -0.336814 -1.771431
2013-01-04 -0.655948 0.484234 1.313306
2013-01-05 0.096433 NaN 1.658917
2013-01-06 1.274731 1.909123 -0.289111
In [14]: df.iloc[0:6].dropna()
Out[14]:
A B C
2013-01-01 0.442692 -0.109415 -0.038182
2013-01-02 1.217950 0.006681 -0.067752
2013-01-04 -0.655948 0.484234 1.313306
2013-01-06 1.274731 1.909123 -0.289111
Using Jeff's dataframe:
import pandas as pd
from numpy.random import randn
df = pd.DataFrame(randn(10,3),columns=list('ABC'),index=pd.date_range('20130101',periods=10))
df.ix[6,'A'] = np.nan
df.ix[6,'B'] = np.nan
df.ix[2,'A'] = np.nan
df.ix[4,'B'] = np.nan
We can replace nans by some number we know is not in the dataframe:
df = df.fillna(999)
If you want to keep only the non-null values without iterating you can do:
df_nona = df.apply(lambda x: list(filter(lambda y: y != 999, x)))
df_na = df.apply(lambda x: list(filter(lambda y: y == 999, x)))
The problem of this approach is that the result are lists so you lose information about the index.
df_nona
A [-1.9804955861, 0.146116306853, 0.359075672435...
B [-1.01963803293, -0.829747654648, 0.6950551455...
C [2.40122968044, 0.79395493777, 0.484201174184,...
dtype: object
Another option is:
df1 = df.dropna()
index_na = df.index ^ df1.index
df_na = df[index_na]
In this case you don't lose info about index, although this is really similar to previous answers.
Hope it helps!