I need to make a loop in pandas faster. It's a time series.
Below code works pretty well but it is slow for massive df.
It iterates through a df and at each first value 0 'zero' of column A (it needs to be only the first zero of a serie; df has many 0 series) calculates the delta (in absolute value) of column B values at one period before and after of the initial value 0 'zero' of column A.
Then it stores the results in a new df with column called 'Delta'
I bet I can do something with loc. but I cannot figure out how.
deltas=[]
indexes = []
i=0
for idx, row in df.iterrows():
if df.A[i] == 0 and df.A[i-1] !=0:
deltas.append(abs(df.B.shift(periods=1)[i] - df.B.shift(periods=-1)[i]))
indexes.append(idx)
i+=1
s_delta = pd.Series(deltas, name="Delta", index = indexes)
df_delta = s_delta.to_frame()
Use assign function to process df in series not per row:
df = df.assign(
n = lambda x: x.B.shift(1),
p = lambda x: x.B.shift(-1),
s_delta= np.abs(x.n-x.p)
)
Then you can modify it using np.where
I am removing a number of records in a pandas data frame which contains diverse combinations of NaN in the 4-columns frame. I have created a function called complete_cases to provide indexes of rows which met the following condition: all columns in the row are NaN.
I have tried this function below:
def complete_cases(dataframe):
indx = []
indx = [x for x in list(dataframe.index) \
if dataframe.loc[x, :].isna().sum() ==
len(dataframe.columns)]
return indx
I am wondering should this is optimal enough or there is a better way to do this.
Absolutely. All you need to do is
df.dropna(axis = 0, how = 'any', inplace = True)
This will remove all rows that have at least one missing value, and updates the data frame "inplace".
I'd recommend to use loc, isna, and any with 'columns' axis, like this:
df.loc[df.isna().any(axis='columns')]
This way you'll filter only the results like the complete.cases in R.
A possible solution:
Count the number of columns with "NA" creating a column to save it
Based on this new column, filter the rows of the data frame as you wish
Remove the (now) unnecessary column
It is possible to do it with a lambda function. For example, if you want to remove rows that have 10 "NA" values:
df['count'] = df.apply(lambda x: 0 if x.isna().sum() == 10 else 1, axis=1)
df = df[df.count != 0]
del df['count']
I'm working with a dataframe. If the column in the dataframe has a certain percentage of blanks I want to append that column into a dictionary (and eventually turn that dictionary into a new dataframe).
features = {}
percent_is_blank = 0.4
for column in df:
x = df[column].isna().mean()
if x < percent_is_blank:
features[column] = ??
new_df = pd.DataFrame.from_dict([features], columns=features.keys())
What would go in the "??"
I think better is filtering with DataFrame.loc:
new_df = df.loc[:, df.isna().mean() < percent_is_blank]
In your solution is possible use:
for column in df:
x = df[column].isna().mean()
if x < percent_is_blank:
features[column] = df[column]
I'm initializing a DataFrame:
columns = ['Thing','Time']
df_new = pd.DataFrame(columns=columns)
and then writing values to it like this:
for t in df.Thing.unique():
df_temp = df[df['Thing'] == t] #filtering the df
df_new.loc[counter,'Thing'] = t #writing the filter value to df_new
df_new.loc[counter,'Time'] = dftemp['delta'].sum(axis=0) #summing and adding that value to the df_new
counter += 1 #increment the row index
Is there are better way to add new values to the dataframe each time without explicitly incrementing the row index with 'counter'?
If I'm interpreting this correctly, I think this can be done in one line:
newDf = df.groupby('Thing')['delta'].sum().reset_index()
By grouping by 'Thing', you have the various "t-filters" from your for-loop. We then apply a sum() to 'delta', but only within the various "t-filtered" groups. At this point, the dataframe has the various values of "t" as the indices, and the sums of the "t-filtered deltas" as a corresponding column. To get to your desired output, we then bump the "t's" into their own column via reset_index().
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