Develop Reference

Trying to compare to values in a pandas dataframe for max value

I've got a pandas dataframe, and I'm trying to fill a new column in the dataframe, which takes the maximum value of two values situated in another column of the dataframe, iteratively. I'm trying to build a loop to do this, and save time with computation as I realise I could probably do it with more lines of code.
for x in ((jac_input.index)):
jac_output['Max Load'][x] = jac_input[['load'][x],['load'][x+1]].max()
However, I keep getting this error during the comparison
IndexError: list index out of range
Any ideas as to where I'm going wrong here? Any help would be appreciated!

Many things are wrong with your current code.
When you do ['abc'][x], x can only take the value 0 and this will return 'abc' as you are slicing a list. Not at all what you expect it to do (I imagine, slicing the Series).
For your code to be valid, you should do something like:
jac_input = pd.DataFrame({'load': [1,0,3,2,5,4]})
for x in jac_input.index:
print(jac_input['load'].loc[x:x+1].max())
output:
1
3
3
5
5
4
Also, when assigning, if you use jac_output['Max Load'][x] = ... you will likely encounter a SettingWithCopyWarning. You should rather use loc: jac_outputLoc[x, 'Max Load'] = .
But you do not need all that, use vectorial code instead!
You can perform rolling on the reversed dataframe:
jac_output['Max Load'] = jac_input['load'][::-1].rolling(2, min_periods=1).max()[::-1]
Or using concat:
jac_output['Max Load'] = pd.concat([jac_input['load'], jac_input['load'].shift(-1)], axis=1).max(1)
output (without assignment):
0 1.0
1 3.0
2 3.0
3 5.0
4 5.0
5 4.0
dtype: float64

Python Return all Columns [duplicate]

Using Python Pandas I am trying to find the Country & Place with the maximum value.
This returns the maximum value:
data.groupby(['Country','Place'])['Value'].max()
But how do I get the corresponding Country and Place name?

Assuming df has a unique index, this gives the row with the maximum value:
In [34]: df.loc[df['Value'].idxmax()]
Out[34]:
Country US
Place Kansas
Value 894
Name: 7
Note that idxmax returns index labels. So if the DataFrame has duplicates in the index, the label may not uniquely identify the row, so df.loc may return more than one row.
Therefore, if df does not have a unique index, you must make the index unique before proceeding as above. Depending on the DataFrame, sometimes you can use stack or set_index to make the index unique. Or, you can simply reset the index (so the rows become renumbered, starting at 0):
df = df.reset_index()

df[df['Value']==df['Value'].max()]
This will return the entire row with max value

I think the easiest way to return a row with the maximum value is by getting its index. argmax() can be used to return the index of the row with the largest value.
index = df.Value.argmax()
Now the index could be used to get the features for that particular row:
df.iloc[df.Value.argmax(), 0:2]

The country and place is the index of the series, if you don't need the index, you can set as_index=False:
df.groupby(['country','place'], as_index=False)['value'].max()
Edit:
It seems that you want the place with max value for every country, following code will do what you want:
df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))

Use the index attribute of DataFrame. Note that I don't type all the rows in the example.
In [14]: df = data.groupby(['Country','Place'])['Value'].max()
In [15]: df.index
Out[15]:
MultiIndex
[Spain Manchester, UK London , US Mchigan , NewYork ]
In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')
In [17]: df.index[1]
Out[17]: ('UK', 'London')
You can also get the value by that index:
In [21]: for index in df.index:
print index, df[index]
....:
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562
Edit
Sorry for misunderstanding what you want, try followings:
In [52]: s=data.max()
In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854

In order to print the Country and Place with maximum value, use the following line of code.
print(df[['Country', 'Place']][df.Value == df.Value.max()])

You can use:
print(df[df['Value']==df['Value'].max()])

Using DataFrame.nlargest.
The dedicated method for this is nlargest which uses algorithm.SelectNFrame on the background, which is a performant way of doing: sort_values().head(n)
x y a b
0 1 2 a x
1 2 4 b x
2 3 6 c y
3 4 1 a z
4 5 2 b z
5 6 3 c z
df.nlargest(1, 'y')
x y a b
2 3 6 c y

import pandas
df is the data frame you create.
Use the command:
df1=df[['Country','Place']][df.Value == df['Value'].max()]
This will display the country and place whose value is maximum.

My solution for finding maximum values in columns:
df.ix[df.idxmax()]
, also minimum:
df.ix[df.idxmin()]

I'd recommend using nlargest for better performance and shorter code. import pandas
df[col_name].value_counts().nlargest(n=1)

I encountered a similar error while trying to import data using pandas, The first column on my dataset had spaces before the start of the words. I removed the spaces and it worked like a charm!!

Pandas - change cell value based on conditions from cell and from column

I have a Dataframe with a lot of "bad" cells. Let's say, they have all -99.99 as values, and I want to remove them (set them to NaN).
This works fine:
df[df == -99.99] = None
But actually I want to delete all these cells ONLY if another cell in the same row is market as 1 (e.g. in the column "Error").
I want to delete all -99.99 cells, but only if df["Error"] == 1.
The most straight-forward solution I thin is something like
df[(df == -99.99) & (df["Error"] == 1)] = None
but it gives me the error:
ValueError: cannot reindex from a duplicate axis
I tried every given solutions on the internet but I cant get it to work! :(
Since my Dataframe is big I don't want to iterate it (which of course, would work, but take a lot of time).
Any hint?

Try using broadcasting while passing numpy values:
# sample data, special value is -99
df = pd.DataFrame([[-99,-99,1], [2,-99,2],
[1,1,1], [-99,0, 1]],
columns=['a','b','Errors'])
# note the double square brackets
df[(df==-99) & (df[['Errors']]==1).values] = np.nan
Output:
a b Errors
0 NaN NaN 1
1 2.0 -99.0 2
2 1.0 1.0 1
3 NaN 0.0 1

At least, this is working (but with column iteration):
for i in df.columns:
df.loc[df[i].isin([-99.99]) & df["Error"].isin([1]), i] = None

Returning date that corresponds with maximum value in pandas dataframe [duplicate]

How can I find the row for which the value of a specific column is maximal?
df.max() will give me the maximal value for each column, I don't know how to get the corresponding row.

Use the pandas idxmax function. It's straightforward:
>>> import pandas
>>> import numpy as np
>>> df = pandas.DataFrame(np.random.randn(5,3),columns=['A','B','C'])
>>> df
A B C
0 1.232853 -1.979459 -0.573626
1 0.140767 0.394940 1.068890
2 0.742023 1.343977 -0.579745
3 2.125299 -0.649328 -0.211692
4 -0.187253 1.908618 -1.862934
>>> df['A'].idxmax()
3
>>> df['B'].idxmax()
4
>>> df['C'].idxmax()
1
Alternatively you could also use numpy.argmax, such as numpy.argmax(df['A']) -- it provides the same thing, and appears at least as fast as idxmax in cursory observations.
idxmax() returns indices labels, not integers.
Example': if you have string values as your index labels, like rows 'a' through 'e', you might want to know that the max occurs in row 4 (not row 'd').
if you want the integer position of that label within the Index you have to get it manually (which can be tricky now that duplicate row labels are allowed).
HISTORICAL NOTES:
idxmax() used to be called argmax() prior to 0.11
argmax was deprecated prior to 1.0.0 and removed entirely in 1.0.0
back as of Pandas 0.16, argmax used to exist and perform the same function (though appeared to run more slowly than idxmax).
argmax function returned the integer position within the index of the row location of the maximum element.
pandas moved to using row labels instead of integer indices. Positional integer indices used to be very common, more common than labels, especially in applications where duplicate row labels are common.
For example, consider this toy DataFrame with a duplicate row label:
In [19]: dfrm
Out[19]:
A B C
a 0.143693 0.653810 0.586007
b 0.623582 0.312903 0.919076
c 0.165438 0.889809 0.000967
d 0.308245 0.787776 0.571195
e 0.870068 0.935626 0.606911
f 0.037602 0.855193 0.728495
g 0.605366 0.338105 0.696460
h 0.000000 0.090814 0.963927
i 0.688343 0.188468 0.352213
i 0.879000 0.105039 0.900260
In [20]: dfrm['A'].idxmax()
Out[20]: 'i'
In [21]: dfrm.iloc[dfrm['A'].idxmax()] # .ix instead of .iloc in older versions of pandas
Out[21]:
A B C
i 0.688343 0.188468 0.352213
i 0.879000 0.105039 0.900260
So here a naive use of idxmax is not sufficient, whereas the old form of argmax would correctly provide the positional location of the max row (in this case, position 9).
This is exactly one of those nasty kinds of bug-prone behaviors in dynamically typed languages that makes this sort of thing so unfortunate, and worth beating a dead horse over. If you are writing systems code and your system suddenly gets used on some data sets that are not cleaned properly before being joined, it's very easy to end up with duplicate row labels, especially string labels like a CUSIP or SEDOL identifier for financial assets. You can't easily use the type system to help you out, and you may not be able to enforce uniqueness on the index without running into unexpectedly missing data.
So you're left with hoping that your unit tests covered everything (they didn't, or more likely no one wrote any tests) -- otherwise (most likely) you're just left waiting to see if you happen to smack into this error at runtime, in which case you probably have to go drop many hours worth of work from the database you were outputting results to, bang your head against the wall in IPython trying to manually reproduce the problem, finally figuring out that it's because idxmax can only report the label of the max row, and then being disappointed that no standard function automatically gets the positions of the max row for you, writing a buggy implementation yourself, editing the code, and praying you don't run into the problem again.

You might also try idxmax:
In [5]: df = pandas.DataFrame(np.random.randn(10,3),columns=['A','B','C'])
In [6]: df
Out[6]:
A B C
0 2.001289 0.482561 1.579985
1 -0.991646 -0.387835 1.320236
2 0.143826 -1.096889 1.486508
3 -0.193056 -0.499020 1.536540
4 -2.083647 -3.074591 0.175772
5 -0.186138 -1.949731 0.287432
6 -0.480790 -1.771560 -0.930234
7 0.227383 -0.278253 2.102004
8 -0.002592 1.434192 -1.624915
9 0.404911 -2.167599 -0.452900
In [7]: df.idxmax()
Out[7]:
A 0
B 8
C 7
e.g.
In [8]: df.loc[df['A'].idxmax()]
Out[8]:
A 2.001289
B 0.482561
C 1.579985

Both above answers would only return one index if there are multiple rows that take the maximum value. If you want all the rows, there does not seem to have a function.
But it is not hard to do. Below is an example for Series; the same can be done for DataFrame:
In [1]: from pandas import Series, DataFrame
In [2]: s=Series([2,4,4,3],index=['a','b','c','d'])
In [3]: s.idxmax()
Out[3]: 'b'
In [4]: s[s==s.max()]
Out[4]:
b 4
c 4
dtype: int64

df.iloc[df['columnX'].argmax()]
argmax() would provide the index corresponding to the max value for the columnX. iloc can be used to get the row of the DataFrame df for this index.

A more compact and readable solution using query() is like this:
import pandas as pd
df = pandas.DataFrame(np.random.randn(5,3),columns=['A','B','C'])
print(df)
# find row with maximum A
df.query('A == A.max()')
It also returns a DataFrame instead of Series, which would be handy for some use cases.

Very simple: we have df as below and we want to print a row with max value in C:
A B C
x 1 4
y 2 10
z 5 9
In:
df.loc[df['C'] == df['C'].max()] # condition check
Out:
A B C
y 2 10

If you want the entire row instead of just the id, you can use df.nlargest and pass in how many 'top' rows you want and you can also pass in for which column/columns you want it for.
df.nlargest(2,['A'])
will give you the rows corresponding to the top 2 values of A.
use df.nsmallest for min values.

The direct ".argmax()" solution does not work for me.
The previous example provided by #ely
>>> import pandas
>>> import numpy as np
>>> df = pandas.DataFrame(np.random.randn(5,3),columns=['A','B','C'])
>>> df
A B C
0 1.232853 -1.979459 -0.573626
1 0.140767 0.394940 1.068890
2 0.742023 1.343977 -0.579745
3 2.125299 -0.649328 -0.211692
4 -0.187253 1.908618 -1.862934
>>> df['A'].argmax()
3
>>> df['B'].argmax()
4
>>> df['C'].argmax()
1
returns the following message :
FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'
will be corrected to return the positional maximum in the future.
Use 'series.values.argmax' to get the position of the maximum now.
So that my solution is :
df['A'].values.argmax()

mx.iloc[0].idxmax()
This one line of code will give you how to find the maximum value from a row in dataframe, here mx is the dataframe and iloc[0] indicates the 0th index.

Considering this dataframe
[In]: df = pd.DataFrame(np.random.randn(4,3),columns=['A','B','C'])
[Out]:
A B C
0 -0.253233 0.226313 1.223688
1 0.472606 1.017674 1.520032
2 1.454875 1.066637 0.381890
3 -0.054181 0.234305 -0.557915
Assuming one want to know the rows where column "C" is max, the following will do the work
[In]: df[df['C']==df['C'].max()])
[Out]:
A B C
1 0.472606 1.017674 1.520032

The idmax of the DataFrame returns the label index of the row with the maximum value and the behavior of argmax depends on version of pandas (right now it returns a warning). If you want to use the positional index, you can do the following:
max_row = df['A'].values.argmax()
or
import numpy as np
max_row = np.argmax(df['A'].values)
Note that if you use np.argmax(df['A']) behaves the same as df['A'].argmax().

Use:
data.iloc[data['A'].idxmax()]
data['A'].idxmax() -finds max value location in terms of row
data.iloc() - returns the row

If there are ties in the maximum values, then idxmax returns the index of only the first max value. For example, in the following DataFrame:
A B C
0 1 0 1
1 0 0 1
2 0 0 0
3 0 1 1
4 1 0 0
idxmax returns
A 0
B 3
C 0
dtype: int64
Now, if we want all indices corresponding to max values, then we could use max + eq to create a boolean DataFrame, then use it on df.index to filter out indexes:
out = df.eq(df.max()).apply(lambda x: df.index[x].tolist())
Output:
A [0, 4]
B [3]
C [0, 1, 3]
dtype: object

what worked for me is:
df[df['colX'] == df['colX'].max()
You then get the row in your df with the maximum value of colX.
Then if you just want the index you can add .index at the end of the query.

Pandas Datframe1 search for match in range of Dataframe2

In the first dataframe, the last two columns (shift_one and shift_two) can be thought of as a guess of a potential true coordinate. Call this df1.
df1:
p_one p_two dist shift_one shift_two
0 Q8_CB Q2_C d_6.71823_Angs 26.821 179.513
1 Q8_CD Q2_C d_4.72003_Angs 179.799 179.514
....
In the second dataframe, call this df2, I have a dataframe of experimental observed coordinates which I denote peaks. It simply is just the coordinates and one more column that is for how intense the signal was, this just needs to be along for the ride.
df2:
A B C
0 31.323 25.814 251106
1 26.822 26.083 690425
2 27.021 179.34 1409596
3 54.362 21.773 1413783
4 54.412 20.163 862750
....
I am aiming to have a method for each guess in df1 to be queried/searched/refrenced in df2, within a range of 0.300 of the initial guess in df1. I then want this to be returned in a new datframe, lets say df3. In this case, we notice there is a match in row 0 of df1 with row 2 of df2.
desired output, df3:
p_one p_two dist shift_one shift_two match match1 match2 match_inten
0 Q8_CB Q2_C d_6.71823_Angs 26.821 179.513 TRUE 27.021 179.34 1409596
1 Q8_CD Q2_C d_4.72003_Angs 179.799 179.514 NaN NaN NaN NaN
....
I have attempted a few things:
(1) O'Reily suggests dealing with bounds in a list in python by using lambda or def (p 78 of python in a nutshell). So I define a bound function like this.
def bounds (value, l=low, h=high)
I was then thinking that I could just add a new column, following the logic used here (https://stackoverflow.com/a/14717374/3767980).
df1['match'] = ((df2['A'] + 0.3 <= df1['shift_one']) or (df2['A'] + 0.3 => df1['shift_one'])
--I'm really struggling with this statement
Next I would just pull the values, which should be trivial.
(2) make new columns for the upper and lower limit, then run a conditional to see if the value is between the two columns.
Finally:
(a) Do you think I should stay in pandas? or should I move over to NumPy or SciPy or just traditional python arrays/lists. I was thinking that a regular python lists of lists too. I'm afraid of NumPy since I have text too, is NumPy exclusive to numbers/matrices only.
(b) Any help would be appreciated. I used biopython for phase_one and phase_two, pandas for phase_three, and I'm not quite sure for this final phase here what is the best library to use.
(c) It is probably fairly obvious that I'm an amateur programer.

The following assumes that the columns to compare have the same names.
def temp(row):
index = df2[((row-df2).abs() < .3).all(axis=1)].index
return df2.loc[index[0], :] if len(index) else [None]*df2.shape[1]
Eg.
df1 = pd.DataFrame([[1,2],[3,4], [5,6]], columns=["d1", "d2"])
df2 = pd.DataFrame([[1.1,1.9],[3.2,4.3]], columns=["d1", "d2"])
df1.apply(temp, axis=1)
produces
d1 d2
0 1.1 1.9
1 3.2 4.3
2 NaN NaN