Develop Reference

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!!

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.

How to remove percent before converting it to integer

I have a dataframe of 2 columns.I want to convert COUNT column to int.It keeps me giving value error:Unable to Parse string "0.58%" at position 0
METRIC COUNT
Scans 125487
No Reads 2541
Diverts 54710
No Code% 0.58%
No Read% 1.25%
df['COUNT'] = df['COUNT'].apply(pd.to_numeric)
How can i remove % before conversion

You can use str.strip:
pd.to_numeric(df.col1.str.strip('%'))
0 1
1 2
2 3
Name: col1, dtype: int64

Try this, I'm assuming that the 0.58% is read in as a string, meaning that the replace function will work to replace '%' with nothing, at which point, it can be converted to a number
import pandas as pd
df = pd.DataFrame({'col1':['1','2','3%']})
df.col1.str.replace('%','').astype(float)

Filtering data in Pandas returns error 'method' object is not iterable

I have a dataset as follows:
I am going to filter rows where the counts value equals 1.
index count
1 4
2 5
3 1
4 1
This is my code:
booleans =[]
for number in df1.count:
if number ==1:
booleans.append (True)
else:
booleans.append (False)
but it has this error:
'method' object is not iterable
I also tried this:
df[df.count==1]
but I had the following error:
KeyError: False
any suggestion?

In your code the problem is with the this part df1.count. Actually, pandas is having a method count() which is used to count the no. of non-NA/null observations across the given axis.
And in your code it returns something like this,
<bound method DataFrame.count of index count
0 1 4
1 2 5
2 3 1
3 4 1>
Instead of it, you can use df[df['count']=='1'] to get what you were looking for.
import pandas as pd
data = {"index":['1','2','3','4'],
"count":['4','5','1','1']}
df = pd.DataFrame(data)
indexes = df[df['count']=='1']
print(indexes)
Output
index count
2 3 1
3 4 1

Count is also a method of pandas DataFrame.
When you do df.count, pandas understands you are calling the count() method, not fetching your column that happens to have the same name. Doing df["count"] would solve your issue.

The standard way to do this is to do the following:
Solution 1
df1[df1["count"]=='1']
Solution 2
However, if you really do want to get a list of booleans you might want to use lambdas:
booleans = list(df1['count'].apply(lambda x:x=='1').values)
You can then use this list to get the result you want like so:
df1[booleans]
This is basically the same thing as solution 1.

Finding substring in pandas frozenset

I'm trying to find a substring in a frozenset, however I'm a bit out of options.
My data structure is a pandas.dataframe (it's from the association_rules from the mlxtend package if you are familiar with that one) and I want to print all the rows where the antecedents (which is a frozenset) include a specific string.
Sample data:
print(rules[rules["antecedents"].str.contains('line', regex=False)])
However whenever I run it, I get an Empty Dataframe.
When I try running only the inner function on my series of rules["antecedents"], I get only False values for all entries. But why is that?

Because dataframe.str.* functions are for string data only. Since your data is not string, it will always be NaN regardless the string representation of it. To prove:
>>> x = pd.DataFrame(np.random.randn(2, 5)).astype("object")
>>> x
0 1 2 3 4
0 -1.17191 -1.92926 -0.831576 -0.0814279 0.099612
1 -1.55183 -0.494855 1.14398 -1.72675 -0.0390948
>>> x[0].str.contains("-1")
0 NaN
1 NaN
Name: 0, dtype: float64
What can you do:
Use apply:
>>> x[0].apply(lambda x: "-1" in str(x))
0 True
1 True
Name: 0, dtype: bool
So your code should write:
print(rules[rules["antecedents"].apply(lambda x: 'line' in str(x))])
You might want to use 'line' in x if you mean an exact match on element