I have a dataframe, where one column consists of Sympy symbols, and another column that consists of values.
import sympy as sym
import pandas as pd
d1,c,bc = sym.symbols("\delta, c, b_c")
values = [(d1,1),(c,2),(bc,3)]
df = pd.DataFrame(values, columns = ['Symbol', 'Value'])
df['Symbol'].sort_values()
When I run the above, I get the error (which was expected, because Sympy symbols aren't sortable themselves, but the actual string contained is sortable.
TypeError: cannot determine truth value of Relational
Sympy symbols are sortable if you can apply the .name method to them. I've done this with numpy arrays and lists of dictionaries:
import numpy as np
values = np.array(values, dtype = [('Symbol','O'),('Value','O')])
values = sorted(values, key = lambda x: x['Symbol'].name)
display(values)
Output:
>>[(\delta, 1), (b_c, 3), (c, 2)]
I'm wondering if it's possible with dataframes because I'd rather not convert to a different format to apply a sort.
I am not sure this is more efficient but perhaps you could create a new column e.g 'Symbol_names' for the data frame with names only and sort by that. You can always drop the column after
df['Symbol_names'] = df['Symbol'].apply(lambda x: x.name)
df = df.sort_values('Symbol_names')\
.drop("Symbol_names", axis=1)\
.reset_index(drop=True) # optional
Related
I have a list of time-series (=pandas dataframe) and want to calculate for each time-series (of a device) the matrixprofile.
One option is to iterate all the devices - which seems to be slow.
A second option would be to group by the devices - and apply a UDF. The problem is now, that the UDF will return 1:1 rows i.e. not a single scalar value per group but the same number of rows will be outputted as the input.
Is it still possible to somehow vectorize this calculation for reach group when 1:1 (or at least non scalar values) are returned?
import pandas as pd
df = pd.DataFrame({
'foo':[1,2,3], 'baz':[1.1, 0.5, 4], 'bar':[1,2,1]
})
display(df)
print('***************************')
# slow version retaining all the rows
for g in df.bar.unique():
print(g)
this_group = df[df.bar == g]
# perform a UDF which needs to have all the values per group
# i.e. for real I want to calculate the matrixprofile for each time-series of a device
this_group['result'] = this_group.baz.apply(lambda x: 1)
display(this_group)
print('***************************')
def my_non_scalar1_1_agg_function(x):
display(pd.DataFrame(x))
return x
# neatly vectorized application of a non_scalar function
# but this fails as: Must produce aggregated value
df = df.groupby(['bar']).baz.agg(my_non_scalar1_1_agg_function)
display(df)
For non-aggregated functions applied to each distinct group that does not return a non-scalar value, you need to iterate method across groups and then compile together.
Therefore, consider a list or dict comprehension using groupby(), followed by concat. Be sure method inputs and returns a full data frame, series, or ndarray.
# LIST COMPREHENSION
df_list = [ myfunction(sub) for index, sub in df.groupby(['group_column']) ]
final_df = pd.concat(df_list)
# DICT COMPREHENSION
df_dict = { index: myfunction(sub) for index, sub in df.groupby(['group_column']) }
final_df = pd.concat(df_dict, ignore_index=True)
Indeed this (see also the link above in the comment) is a way to get it to work in a faster/more desired way. Perhaps there is even a better alternative
import pandas as pd
df = pd.DataFrame({
'foo':[1,2,3], 'baz':[1.1, 0.5, 4], 'bar':[1,2,1]
})
display(df)
grouped_df = df.groupby(['bar'])
altered = []
for index, subframe in grouped_df:
display(subframe)
subframe = subframe# obviously we need to apply the UDF here - not the idempotent operation (=doing nothing)
altered.append(subframe)
print (index)
#print (subframe)
pd.concat(altered, ignore_index=True)
#pd.DataFrame(altered)
I have a large dataframe and would like to update specific values at known row and column indices. I would like to do this without an explicit for loop.
For example:
import string
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 10), index = range(10), columns = list(string.ascii_lowercase)[:10])
I have arbitrary arrays of indexes, columns, and values that I would like to use to update df. For example:
update_values = [0,-2,-3]
update_index = [3,5,7]
update_columns = ["d","g","i"]
I can loop over the arrays to update the original dataframe:
for i,j,v in zip(update_index, update_columns, update_values):
df.loc[i,j] = v
but would like to use a technique not involving an explicit for loop.
Use the underlying numpy values
indexes = map(df.columns.get_loc, update_columns)
df.values[update_index, list(indexes)] = update_values
try using loc which is used to specify the needed indexes and columns names loc[[index_names], [columns_names]]
df.loc[[3,5,7], ["d","g","i"]] = [0,-2,-3]
I want to split data in two columns from a data frame and construct new columns using this data.
My data frame is,
dfc = pd.DataFrame( {"A": ["GT:DP:RO:QR:AO:QA:GL", "GT:DP:RO:QR:AO:QA:GL", "GT:DP:RO:QR:AO:QA:GL", "GT:DP:GL", "GT:DP:GL"], "B": ["0/1:71:43:1363:28:806:-71.1191,0,-121.278", "0/1:71:43:1363:28:806:-71.1191,0,-121.278", "0/1:71:43:1363:28:806:-71.1191,0,-121.278", "1/1:49:-103.754,0,-3.51307", "1/1:49:-103.754,0,-3.51307"]} )
I want individual columns named GT, DP, RO, QR, AO, QA, GL with values from column B
I want to produce output as,
We can split the two columns using a = df.A.str.split(":", expand = True)and b = df.B.str.split(":", expand = True) to get two individual data frames. These can be merged with c = pd.merge(a, b, left_index = True, right_index = True) to get all desired data. But, not in the format as expected.
Any suggestions ? I think better way can be using split on both columns A and B and then creating a dictcolumn with values from A as key and B as values. Then this column can be converted to data frame.
Thanks
Use an OrderedDict to preserve the order after creating a dict mapping of the two concerned columns of the dataframe split on the sep ":", flattened to a list.
Feed this to the dataframe constructor later.
from collections import OrderedDict
L = dfc.apply(
lambda x: OrderedDict(zip(x['A'].split(':'), x['B'].split(':'))), 1).tolist()
pd.DataFrame(L)
I'm going to split everything by ':'. But I have 2 columns. If I stack first, I get a series in which I can more easily use str.split
I now have a split series in which I can group by level=0 which is the original index.
I zip and dict to get series like structures with the original column A as the indices and B as the values.
unstack and I'm done.
gb = dfc.stack().str.split(':').groupby(level=0)
gb.apply(lambda x: dict(zip(*x))).unstack()
I have a dataframe. For each row of the dataframe: I need to read values from two column indexes, pass these values to a set of equations, enter the result of each equation into its own column index in the same row, go to the next row and repeat.
After reading the responses to similar questions I tried:
import pandas as pd
DF = pd.read_csv("...")
Equation_1 = f(x, y)
Equation_2 = g(x, y)
for index, row in DF.iterrows():
a = DF[m]
b = DF[n]
DF[p] = Equation_1(a, b)
DF[q] = Equation_2(a, b)
Rather than iterating over DF, reading and entering new values for each row, this codes iterates over DF and enters the same values for each row. I am not sure what I am doing wrong here.
Also, from what I have read it is actually faster to treat the DF as a NumPy array and perform the calculation over the entire array at once rather than iterating. Not sure how I would go about this.
Thanks.
Turns out that this is extremely easy. All that must be done is to define two variables and assign the desired columns to them. Then set "the row to be replaced" equivalent to the equation containing the variables.
Pandas already knows that it must apply the equation to every row and return each value to its proper index. I didn't realize it would be this easy and was looking for more explicit code.
e.g.,
import pandas as pd
df = pd.read_csv("...") # df is a large 2D array
A = df[0]
B = df[1]
f(A,B) = ....
df[3] = f(A,B)
# If your equations are simple enough, do operations column-wise in Pandas:
import pandas as pd
test = pd.DataFrame([[1,2],[3,4],[5,6]])
test # Default column names are 0, 1
test[0] # This is column 0
test.icol(0) # This is COLUMN 0-indexed, returned as a Series
test.columns=(['S','Q']) # Column names are easier to use
test #Column names! Use them column-wise:
test['result'] = test.S**2 + test.Q
test # results stored in DataFrame
# For more complicated stuff, try apply, as in Python pandas apply on more columns :
def toyfun(df):
return df[0]-df[1]**2
test['out2']=test[['S','Q']].apply(toyfun, axis=1)
# You can also define the column names when you generate the DataFrame:
test2 = pd.DataFrame([[1,2],[3,4],[5,6]],columns = (list('AB')))
I have this code using Pandas in Python:
all_data = {}
for ticker in ['FIUIX', 'FSAIX', 'FSAVX', 'FSTMX']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2015')
prices = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})
returns = prices.pct_change()
I know I can run a regression like this:
regs = sm.OLS(returns.FIUIX,returns.FSTMX).fit()
but how can I do this for each column in the dataframe? Specifically, how can I iterate over columns, in order to run the regression on each?
Specifically, I want to regress each other ticker symbol (FIUIX, FSAIX and FSAVX) on FSTMX, and store the residuals for each regression.
I've tried various versions of the following, but nothing I've tried gives the desired result:
resids = {}
for k in returns.keys():
reg = sm.OLS(returns[k],returns.FSTMX).fit()
resids[k] = reg.resid
Is there something wrong with the returns[k] part of the code? How can I use the k value to access a column? Or else is there a simpler approach?
for column in df:
print(df[column])
You can use iteritems():
for name, values in df.iteritems():
print('{name}: {value}'.format(name=name, value=values[0]))
This answer is to iterate over selected columns as well as all columns in a DF.
df.columns gives a list containing all the columns' names in the DF. Now that isn't very helpful if you want to iterate over all the columns. But it comes in handy when you want to iterate over columns of your choosing only.
We can use Python's list slicing easily to slice df.columns according to our needs. For eg, to iterate over all columns but the first one, we can do:
for column in df.columns[1:]:
print(df[column])
Similarly to iterate over all the columns in reversed order, we can do:
for column in df.columns[::-1]:
print(df[column])
We can iterate over all the columns in a lot of cool ways using this technique. Also remember that you can get the indices of all columns easily using:
for ind, column in enumerate(df.columns):
print(ind, column)
You can index dataframe columns by the position using ix.
df1.ix[:,1]
This returns the first column for example. (0 would be the index)
df1.ix[0,]
This returns the first row.
df1.ix[:,1]
This would be the value at the intersection of row 0 and column 1:
df1.ix[0,1]
and so on. So you can enumerate() returns.keys(): and use the number to index the dataframe.
A workaround is to transpose the DataFrame and iterate over the rows.
for column_name, column in df.transpose().iterrows():
print column_name
Using list comprehension, you can get all the columns names (header):
[column for column in df]
Based on the accepted answer, if an index corresponding to each column is also desired:
for i, column in enumerate(df):
print i, df[column]
The above df[column] type is Series, which can simply be converted into numpy ndarrays:
for i, column in enumerate(df):
print i, np.asarray(df[column])
I'm a bit late but here's how I did this. The steps:
Create a list of all columns
Use itertools to take x combinations
Append each result R squared value to a result dataframe along with excluded column list
Sort the result DF in descending order of R squared to see which is the best fit.
This is the code I used on DataFrame called aft_tmt. Feel free to extrapolate to your use case..
import pandas as pd
# setting options to print without truncating output
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)
import statsmodels.formula.api as smf
import itertools
# This section gets the column names of the DF and removes some columns which I don't want to use as predictors.
itercols = aft_tmt.columns.tolist()
itercols.remove("sc97")
itercols.remove("sc")
itercols.remove("grc")
itercols.remove("grc97")
print itercols
len(itercols)
# results DF
regression_res = pd.DataFrame(columns = ["Rsq", "predictors", "excluded"])
# excluded cols
exc = []
# change 9 to the number of columns you want to combine from N columns.
#Possibly run an outer loop from 0 to N/2?
for x in itertools.combinations(itercols, 9):
lmstr = "+".join(x)
m = smf.ols(formula = "sc ~ " + lmstr, data = aft_tmt)
f = m.fit()
exc = [item for item in x if item not in itercols]
regression_res = regression_res.append(pd.DataFrame([[f.rsquared, lmstr, "+".join([y for y in itercols if y not in list(x)])]], columns = ["Rsq", "predictors", "excluded"]))
regression_res.sort_values(by="Rsq", ascending = False)
I landed on this question as I was looking for a clean iterator of columns only (Series, no names).
Unless I am mistaken, there is no such thing, which, if true, is a bit annoying. In particular, one would sometimes like to assign a few individual columns (Series) to variables, e.g.:
x, y = df[['x', 'y']] # does not work
There is df.items() that gets close, but it gives an iterator of tuples (column_name, column_series). Interestingly, there is a corresponding df.keys() which returns df.columns, i.e. the column names as an Index, so a, b = df[['x', 'y']].keys() assigns properly a='x' and b='y'. But there is no corresponding df.values(), and for good reason, as df.values is a property and returns the underlying numpy array.
One (inelegant) way is to do:
x, y = (v for _, v in df[['x', 'y']].items())
but it's less pythonic than I'd like.
Most of these answers are going via the column name, rather than iterating the columns directly. They will also have issues if there are multiple columns with the same name. If you want to iterate the columns, I'd suggest:
for series in (df.iloc[:,i] for i in range(df.shape[1])):
...
assuming X-factor, y-label (multicolumn):
columns = [c for c in _df.columns if c in ['col1', 'col2','col3']] #or '..c not in..'
_df.set_index(columns, inplace=True)
print( _df.index)
X, y = _df.iloc[:,:4].values, _df.index.values