Simple Question Here:
b = 8143.1795845088482
d = 14723.523658084257
My Df called final:
Words score
This 90374.98788
is 80559.4495
a 43269.67002
sample 34535.01172
output Very Low
I want to replace all the scores with either 'very low', 'low', 'medium', or 'high' based on whether they fall between quartile ranges.
something like this works:
final['score'][final['score'] <= b] = 'Very Low' #This is shown in the example above
but when I try to play this immediately after it doesn't work:
final['score'][final['score'] >= b] and final['score'][final['score'] <= d] = 'Low'
This gives me the error: cannot assign operator. Anyone know what I am missing?
Firstly you must use the bitwise (e.g. &, | instead of and , or) operators as you are comparing arrays and therefore all the values and not a single value (it becomes ambiguoous to compare arrays like this plus you cannot override the global and operator to behave like you want), secondly you must use parentheses around multiple conditions due to operator precendence.
Finally you are performing chain indexing which may or may not work and will raise a warning, to set your column value use loc like this:
In [4]:
b = 25
d = 50
final.loc[(final['score'] >= b) & (final['score'] <= d), 'score'] = 'Low'
final
Out[4]:
Words score
0 This 10
1 is Low
2 for Low
3 You 704
If your DataFrame's scores were all floats,
In [234]: df
Out[234]:
Words score
0 This 90374.98788
1 is 80559.44950
2 a 43269.67002
3 sample 34535.01172
then you could use pd.qcut to categorize each value by its quartile:
In [236]: df['quartile'] = pd.qcut(df['score'], q=4, labels=['very low', 'low', 'medium', 'high'])
In [237]: df
Out[237]:
Words score quartile
0 This 90374.98788 high
1 is 80559.44950 medium
2 a 43269.67002 low
3 sample 34535.01172 very low
DataFrame columns have a dtype. When the values are all floats, then it has a float dtype, which can be very fast for numerical calculations. When the values are a mixture of floats and strings then the dtype is object, which mean each value is a Python object. While this gives the values a lot of flexibility, it is also very slow since every operation ultimately resorts back to calling a Python function instead of a NumPy/Panda C/Fortran/Cython function. Thus you should try to avoid mixing floats and strings in a single column.
Related
I have a problem where I would like to count the number of times the current value has not changed in a dataframe over rolling periods.
For example:
df = pd.DataFrame({'col':list('aaaabbab')})
would somehow give output of
0
1
2
3
0
1
0
0
I have been trying something along the following
df['col'] = df['col'] == df['col'].shift(1)
df.rolling(window=3).sum().reset_index(drop=True, level=0)
I have added in the rolling as I will want to look at the full data set in terms of rolling periods but even without having it over rolling periods I can not quite figure out the logic.
I am not sure if I am missing something simple or this may not be possible using shift
You need to generate a grouper for the change in values. For this compare each value with the previous one and apply a cumsum. This gives you groups in the itertools.groupby style ([1, 1, 1, 1, 2, 2, 3, 4]), finally group and apply a cumcount.
df['count'] = (df.groupby(df['col'].ne(df['col'].shift()).cumsum())
.cumcount()
)
output:
col count
0 a 0
1 a 1
2 a 2
3 a 3
4 b 0
5 b 1
6 a 0
7 b 0
edit: for fun here is a solution using itertools (much faster):
from itertools import groupby, chain
df['count'] = list(chain(*(list(range(len(list(g))))
for _,g in groupby(df['col']))))
NB. this runs much faster (88 µs vs 707 µs on the provided example)
I can't comment so just to add some more to #mozway answer.
My goal was to count consecutives value for an entire huge dataframe effectively.
The pb I encounter is that by construction
np.nan == np.nan
will return False so you could have a whole column full of only NaN and yet the counter will be at 0.
A simple workaround would be to replace all NaN in your df by a value not already in it.
For instance in the case of a float dataset you could do
df.fillna('NA')
which will work but by changing the dtype of your columns to Object the following code will be much slower (20x on my set up).
I would rather advised something like :
all_values = list(np.unique(np.array(df)))
all_values = [a for a in all_values if a==a]
unik_val = min(all_values)-1
temp = df.fillna(unik_val).copy()
from itertools import groupby, chain
for col in temp.columns:
temp[col] = list(chain(*(list(range(len(list(g))))
for _,g in groupby(temp[col]))))
count_df
The problem is how to find out the correlation between two categorical [series] items?
the situation is like that i have to find out the correlation between HAVING_CPOX and NUM_VECILLA_veccine
Given among children
the main catch is that in HAVING CPOX COLUMNS have 4 unique value
1-Having cpox
2-not having cpox
99- may be NULL
7 i don't know
in df['P_NUMVRC'] : unique value is [1, 2, 3, 0, Nan,]
two different distinct series SO how do find put them together and find the correlation
I use value_counts for having frequency of each?
1 13781
2 213
3 1
Name: P_NUMVRC, dtype: int64
For having_cpox columns
2 27955
1 402
77 105
99 3
Name: HAD_CPOX, dtype: int64
the requirement is like this
A positive correlation (e.g., corr > 0) means that an increase in had _ch
ickenpox_column (which means more no’s) would also increase the values of
um_chickenpox_vaccine_column (which means more doses of vaccine). If there
is a negative correlation (e.g., corr < 0), it indicates that having had
chickenpox is related to an increase in the number of vaccine doses.
I think what you are looking for is using np.corrcoef. It receives two (in your case - 1 dimensional) arrays, and returns the Pearson Correlation (for more details see: https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html).
So basically:
valid_df = df.query('HAVING_CPOX < 3')
valid_df['HAVING_CPOX'].apply(lambda x: x == 1, inplace=True)
corr = np.corrcoef(valid_df['HAVING_CPOX'], valid_df['P_NUMVRC'])
What I did is first get rid of the 99's and 7's since you can't really rely on those. Then I changed the HAVING_CPOX to be binary (0 is "has no cpox" and 1 is "has cpox"), so that the correlation makes sense. Then I used corrcoef from numpy's implementation.
I am trying to create a new column in Pandas dataframe. If the other two date columns in my dataframe share the same month, then this new column should have 1 as a value, otherwise 0. Also, I need to check that ids match my other list of ids that I have saved previously in another place and mark those only with 1. I have some code but it is useless since I am dealing with almost a billion of rows.
my_list_of_ids = df[df.bool_column == 1].id.values
def my_func(date1, date2):
for id_ in df.id:
if id_ in my_list_of_ids:
if date1.month == date2.month:
my_var = 1
else:
my_var = 0
else:
my_var = 0
return my_var
df["new_column"] = df.progress_apply(lambda x: my_func(x['date1'], x['date2']), axis=1)
Been waiting for 30 minutes and still 0%. Any help is appreciated.
UPDATE (adding an example):
id | date1 | date2 | bool_column | new_column |
id1 2019-02-13 2019-04-11 1 0
id1 2019-03-15 2019-04-11 0 0
id1 2019-04-23 2019-04-11 0 1
id2 2019-08-22 2019-08-11 1 1
id2 ....
id3 2019-09-01 2019-09-30 1 1
.
.
.
What I need to do is save the ids that are 1 in my bool_column, then I am looping through all of the ids in my dataframe and checking if they are in the previously created list (= 1). Then I want to compare month and the year of date1 and date2 columns and if they are the same, create a new_column with a value 1 where they mach, otherwise, 0.
The pandas way to do this is
mask = ((df['date1'].month == df['date2'].month) & (df['id'].isin(my_list_of_ids)))
df['new_column'] = mask.replace({False: 0, True: 1})
Since you have a large data-set, this will take time, but should be faster than using apply
The best way to deal with the month match is to use vectorization in pandas and do this:
new_column = (df.date1.dt.month == df.date2.dt.month).astype(int)
That is, avoid using apply() over the DataFrame (which will probably be iterative) and take advantage of the underlying numpy vectorization. The gateway to such functionality is almost always in families of Series functions and properties, like the dt family for dates, str family for strings, and so forth.
Luckily, you have pre-computed the id_list membership in your bool_column, so to add membership as a criterion, just do this:
new_column = ((df.date1.dt.month == df.date2.dt.month) & df.bool_column).astype(int)
Once again, the & of two Series takes advantage of vectorization. You stay inside boolean space till the end, then cast to int with astype(int). Reviewing your code, it occurs to me that the iterative checking of your id_list may be the real performance hit here, even more so than the DataFrame.apply(). Whatever you do, avoid at all costs iterating your id_list at each row, since you already have a vector denoting membership in your bool_column.
By the way I believe there's a tiny error in your example data, the new_column value for your third row should be 0, since your bool_column value there is 0.
I have the following df,
A id
[ObjectId('5abb6fab81c0')] 0
[ObjectId('5abb6fab81c3'),ObjectId('5abb6fab81c4')] 1
[ObjectId('5abb6fab81c2'),ObjectId('5abb6fab81c1')] 2
I like to flatten each list in A, and assign its corresponding id to each element in the list like,
A id
ObjectId('5abb6fab81c0') 0
ObjectId('5abb6fab81c3') 1
ObjectId('5abb6fab81c4') 1
ObjectId('5abb6fab81c2') 2
ObjectId('5abb6fab81c1') 2
I think the comment is coming from this question ? you can using my original post or this one
df.set_index('id').A.apply(pd.Series).stack().reset_index().drop('level_1',1)
Out[497]:
id 0
0 0 1.0
1 1 2.0
2 1 3.0
3 1 4.0
4 2 5.0
5 2 6.0
Or
pd.DataFrame({'id':df.id.repeat(df.A.str.len()),'A':df.A.sum()})
Out[498]:
A id
0 1 0
1 2 1
1 3 1
1 4 1
2 5 2
2 6 2
This probably isn't the most elegant solution, but it works. The idea here is to loop through df (which is why this is likely an inefficient solution), and then loop through each list in column A, appending each item and the id to new lists. Those two new lists are then turned into a new DataFrame.
a_list = []
id_list = []
for index, a, i in df.itertuples():
for item in a:
a_list.append(item)
id_list.append(i)
df1 = pd.DataFrame(list(zip(alist, idlist)), columns=['A', 'id'])
As I said, inelegant, but it gets the job done. There's probably at least one better way to optimize this, but hopefully it gets you moving forward.
EDIT (April 2, 2018)
I had the thought to run a timing comparison between mine and Wen's code, simply out of curiosity. The two variables are the length of column A, and the length of the list entries in column A. I ran a bunch of test cases, iterating by orders of magnitude each time. For example, I started with A length = 10 and ran through to 1,000,000, at each step iterating through randomized A entry list lengths of 1-10, 1-100 ... 1-1,000,000. I found the following:
Overall, my code is noticeably faster (especially at increasing A lengths) as long as the list lengths are less than ~1,000. As soon as the randomized list length hits the ~1,000 barrier, Wen's code takes over in speed. This was a huge surprise to me! I fully expected my code to lose every time.
Length of column A generally doesn't matter - it simply increases the overall execution time linearly. The only case in which it changed the results was for A length = 10. In that case, no matter the list length, my code ran faster (also strange to me).
Conclusion: If the list entries in A are on the order of a few hundred elements (or less) long, my code is the way to go. But if you're working with huge data sets, use Wen's! Also worth noting that as you hit the 1,000,000 barrier, both methods slow down drastically. I'm using a fairly powerful computer, and each were taking minutes by the end (it actually crashed on the A length = 1,000,000 and list length = 1,000,000 case).
Flattening and unflattening can be done using this function
def flatten(df, col):
col_flat = pd.DataFrame([[i, x] for i, y in df[col].apply(list).iteritems() for x in y], columns=['I', col])
col_flat = col_flat.set_index('I')
df = df.drop(col, 1)
df = df.merge(col_flat, left_index=True, right_index=True)
return df
Unflattening:
def unflatten(flat_df, col):
flat_df.groupby(level=0).agg({**{c:'first' for c in flat_df.columns}, col: list})
After unflattening we get the same dataframe except column order:
(df.sort_index(axis=1) == unflatten(flatten(df)).sort_index(axis=1)).all().all()
>> True
To create unique index you can call reset_index after flattening
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.