Develop Reference

pandas df masking specific row by list

I have pandas df which has 7000 rows * 7 columns. And I have list (row_list) that consists with the value that I want to filter out from df.
What I want to do is to filter out the rows if the rows from df contain the corresponding value in the list.
This is what I got when I tried,
"Empty DataFrame
Columns: [A,B,C,D,E,F,G]
Index: []"
df = pd.read_csv('filename.csv')
df1 = pd.read_csv('filename1.csv', names = 'A')
row_list = []
for index, rows in df1.iterrows():
my_list = [rows.A]
row_list.append(my_list)
boolean_series = df.D.isin(row_list)
filtered_df = df[boolean_series]
print(filtered_df)

replace
boolean_series = df.RightInsoleImage.isin(row_list)
with
boolean_series = df.RightInsoleImage.isin(df1.A)
And let us know the result. If it doesn't work show a sample of df and df1.A

(1) generating separate dfs for each condition, concat, then dedup (slow)
(2) a custom function to annotate with bool column (default as False, then annotated True if condition is fulfilled), then filter based on that column
(3) keep a list of indices of all rows with your row_list values, then filter using iloc based on your indices list
Without an MRE, sample data, or a reason why your method didn't work, it's difficult to provide a more specific answer.

pandas apply function to each group (output is not really an aggregation)

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)

Create a dictionary from pandas empty dataframe with only column names

I have a pandas data frame with only two column names( single row, which can be also considered as headers).I want to make a dictionary out of this with the first column being the value and the second column being the key.I already tried the
to.dict() method, but it's not working as it's an empty dataframe.
Example
df=|Land |Norway| to {'Land': Norway}
I can change the pandas data frame to some other type and find my way around it, but this question is mostly to learn the best/different/efficient approach for this problem.
For now I have this as the solution :
dict(zip(a.iloc[0:0,0:1],a.iloc[0:0,1:2]))
Is there any other way to do this?

Here's a simple way convert the columns to a list and a list to a dictionary
def list_to_dict(a):
it = iter(a)
ret_dict = dict(zip(it, it))
return ret_dict
df = pd.DataFrame([], columns=['Land', 'Normway'])
dict_val = list_to_dict(df.columns.to_list())
dict_val # {'Land': 'Normway'}

Very manual solution
df = pd.DataFrame(columns=['Land', 'Norway'])
df = pd.DataFrame({df.columns[0]: df.columns[1]}, index=[0])
If you have any number of columns and you want each sequential pair to have this transformation, try:
df = pd.DataFrame(dict(zip(df.columns[::2], df.columns[1::2])), index=[0])
Note: You will get an error if your DataFrame does not have at least two columns.

Populating a column based off of values in another column

Hi I am working with pandas to manipulate some lab data. I currently have a data frame with 5 columns.
The first three columns(Analyte,CAS NO(1), and Value) are in the correct order.
The last two columns(CAS NO 2 and Value 2) are not.
Is there a way to align CAS No(2) and Value(2) with the first three columns based off of matching CAS Numbers(aka CAS NO(2)=CAS(NO1).
I am new to python and pandas. Thank you for your help

you can reorder the columns by reassigning the df variable as a slice of itself indexed on a list whose entries are the column names in question.
colidx = ['Analyte', 'CAS NO(1)', 'CAS NO(2)']
df = df[colidx]

Better provide input data in text format so we can copy-paste it. I understand you question like this: You need to sort two last columns together, so that CAS NO(2) matches CAS NO(1).
Since CAS NO(2)=CAS(NO1) you then do not need duplicated CAS NO(2) column, right?
Split off two last columns and make a Series from it, then convert that series to dict, and use that dict to map new values.
# Split 2 last columns and assign index.
df_tmp = df[['CAS NO(2)', 'Value(2)']]
df_tmp = df_tmp.set_index('CAS NO(2)')
# Keep only 3 first columns of original dataframe
df = df[['Analyte',' CASNo(1)', 'Value(1)']]
# Now copy the CasNO(1) to CAS NO(2)
df['CAS NO(2)'] = df['CasNO(1)']
# Now create Value(2) column on original dataframe
df['Value(2)'] = df['CASNo(1)'].map(df_tmp.to_dict()['Value(2)'])

Try the following:
import pandas as pd
import numpy as np
#create an example of your table
list_CASNo1 = ['71-43-2', '100-41-4', np.nan, '1634-04-4']
list_Val1 = [np.nan]*len(list_CASNo1)
list_CASNo2 = [np.nan, np.nan, np.nan, '100-41-4']
list_Val2 = [np.nan, np.nan, np.nan, '18']
df = pd.DataFrame(zip(list_CASNo1, list_Val1, list_CASNo2, list_Val2), columns =['CASNo(1)','Value(1)','CAS NO(2)','Value(2)'], index = ['Benzene','Ethylbenzene','Gasonline Range Organics','Methyl-tert-butyl ether'])
#split the data to two dataframes
df1 = df[['CASNo(1)','Value(1)']]
df2 = df[['CAS NO(2)','Value(2)']]
#merge df2 to df1 based on the specified columns
#reset_index and set_index will take care
#that df_adjusted will have the same index names as df1
df_adjusted = df1.reset_index().merge(df2.dropna(),
how = 'left',
left_on = 'CASNo(1)',
right_on = 'CAS NO(2)').set_index('index')
but be careful with duplicates in your columns, those will cause the merge to fail..

How to iterate over columns of pandas dataframe to run regression

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