I have a Pandas dataframe df1 with x rows. I also have a numpy.ndarray n1 with x rows. n1 has only one column, with values of either 0, or 1. I want to pick only the first column of the dataframe df1, where the corresponding ndarray column has value 1. How can this be done ?
The use case is like this :- I have a invoice dataframe, whose first column is the customer code. I also have a ndarray which is the output of a scikit churn prediction, based on this invoice dataframe as input. The ndarray has 1 for those invoices which has symptoms of churn and 0 for invoices which do not churn. So i want to extract customers who churn. Ofcourse the output will have repeated values of same customer, but that can be filtered.
You can convert your indicators to booleans and then use boolean filtering.
df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
n1 = np.array([0, 1, 1])
>>> df1
a b
0 1 4
1 2 5
2 3 6
>>> df1[n1.astype('bool')]
a b
1 2 5
2 3 6
Related
here is the instance:
Column 1 Column 2 Column 3
2.99 4 Price OK
1.99 4 Price below limit
12.99
5.99 6 Price OK
1.99 6 Price below limit
8.99 6 Price OK
So for Power BI context Column 2 is a custom column from power query, the goal is to set a threshold value for column 2 pack size, in this instance pack size of 4 needs to check for minimum price of $2.99 (higher is ok), below the price should be below limit, in instance of column 2 blanks (result should also be blank). In the instance of size 6 the minimum price to check for is 5.99.
Is there a decent way to go about this?
Let's do this in two steps. First, create a column MinPrice that defines your minimum prices.
if [Column 2] = 4 then 2.99
else if [Column 2] = 6 then 5.99
else null
Then create a column that compares the actual and the minimal
if [Column 1] = null or [Column 2] = null then null
else if [Column 1] < [MinPrice] then "Price below limit"
else "Price OK"
If you have a bunch of unique values in Column 2 that you need to create rules for, then create a reference table that you can merge onto your original table and expand the MinPrice column instead of the first step stated above.
Column2 MinPrice
-----------------
4 2.99
6 5.99
8 7.99
...
I have a pandas data frame and I have a list of values. I want to keep all the rows from my original DF that have a certain column value belonging to my list of values. However my list that I want to choose my rows from have repeated values. Each time I encounter the same values again I want to add the rows with that column values again to my new data frame.
lets say my frames name is: with_prot_choice_df and my list is: with_prot_choices
if I issue the following command:
with_prot_choice_df = with_df[with_df[0].isin(with_prot_choices)]
then this will only keep the rows once (as if for only unique values in the list).
I don't want to do this with for loops since I will repeat the process many times and it will be extremely time consuming.
Any advice will be appreciated. Thanks.
I'm adding an example here:
let's say my data frame is:
col1 col2
a 1
a 6
b 2
c 3
d 4
and my list is:
lst = [a,b,a,a]
I want my new data frame, new_df to be:
new_df
col1 col2
a 1
a 6
b 2
a 1
a 6
a 1
a 6
Seems like you need reindex
df.set_index('col1').reindex(lst).reset_index()
Out[224]:
col1 col2
0 a 1
1 b 2
2 a 1
3 a 1
Updated
df.merge(pd.DataFrame({'col1':lst}).reset_index()).sort_values('index').drop('index',1)
Out[236]:
col1 col2
0 a 1
3 a 6
6 b 2
1 a 1
4 a 6
2 a 1
5 a 6
I need to build a DataFrame with a very specific structure. Yield curve values as the data, a single date as the index, and days to maturity as the column names.
In[1]: yield_data # list of size 38, with yield values
Out[1]:
[0.096651956137087325,
0.0927199778042056,
0.090000225505577847,
0.088300016028163508,...
In[2]: maturity_data # list of size 38, with days until maturity
Out[2]:
[6,
29,
49,
70,...
In[3]: today
Out[3]:
Timestamp('2017-07-24 00:00:00')
Then I try to create the DataFrame
pd.DataFrame(data=yield_data, index=[today], columns=maturity_data)
but it returns the error
ValueError: Shape of passed values is (1, 38), indices imply (38, 1)
I tried using the transpose of these lists, but it does not allow to transpose them.
how can I create this DataFrame?
IIUC, I think you want a dataframe with a single row, you need to reshape your data input list into a list of list.
yield_data = [0.09,0.092, 0.091]
maturity_data = [6,10,15]
today = pd.to_datetime('2017-07-25')
pd.DataFrame(data=[yield_data],index=[today],columns=maturity_data)
Output:
6 10 15
2017-07-25 0.09 0.092 0.091
I'm having trouble with calculating the mean of Timestamps.
I have a few values with Timestamps in my Data Frame, and I want to aggregate the values into a single value with the sum of all values and the weighted mean of the appropriate Timestamps
My input is:
Timestamp Value
ID
0 2013-02-03 13:39:00 79
0 2013-02-03 14:03:00 19
1 2013-02-04 11:36:00 2
2 2013-02-04 12:07:00 2
3 2013-02-04 14:04:00 1
And I want to aggregate the data using the ID index.
I was able to sum the Values using
manp_func = {'Value':['sum'] }
new_table =table.groupby(level='ID).agg(manp_func)
but, how can I find the weighted mean of the Timestamps related to the values?
Thanks
S.A
agg = lambda x: (x['Timestamp'].astype('i8') * (x['Value'].astype('f8') / x['Value'].sum())).sum()
new_table = table.groupby(level='ID').apply(agg).astype('i8').astype('datetime64[ns]')
Output of new_table
ID
0 2013-02-03 13:43:39.183673344
2 2013-02-04 11:51:30.000000000
3 2013-02-04 14:04:00.000000000
dtype: datetime64[ns]
The main idea is to compute the weighted average as normal, but there are a couple of subtleties:
You have to convert the datetime64[ns] to an integer offset first because multiplication is not defined between those two types. Then you have to convert it back.
Calculating the weighted sum as sum(a*w)/sum(w) will result in overflow (a*w is too large to be represented as an 8-byte integer), so it has to be calculated as sum(a*(w/sum(w)).
Preparing a sample dataframe:
# Initiate dataframe
date_var = "date"
df = pd.DataFrame(data=[['A', '2018-08-05 17:06:01'],
['A', '2018-08-05 17:06:02'],
['A', '2018-08-05 17:06:03'],
['B', '2018-08-05 17:06:07'],
['B', '2018-08-05 17:06:09'],
['B', '2018-08-05 17:06:11']],
columns=['column', date_var])
# Convert date-column to proper pandas Datetime-values/pd.Timestamps
df[date_var] = pd.to_datetime(df[date_var])
Extraction of the desired average Timestamp-value:
# Extract the numeric value associated to each timestamp (epoch time)
# NOTE: this is being accomplished via accessing the .value - attribute of each Timestamp in the column
In:
[tsp.value for tsp in df[date_var]]
Out:
[
1533488761000000000, 1533488762000000000, 1533488763000000000,
1533488767000000000, 1533488769000000000, 1533488771000000000
]
# Use this to calculate the mean, then convert the result back to a timestamp
In:
pd.Timestamp(np.nanmean([tsp.value for tsp in df[date_var]]))
Out:
Timestamp('2018-08-05 17:06:05.500000')
I'm new to using pandas and I'm trying to make a dataframe with historical weather data.
The keys are the day of the year (ex. Jan 1) and the values are lists of temperatures from those days over several years.
I want to make a dataframe that is formatted like this:
... Jan1 Jan2 Jan3 etc
1 temp temp temp etc
2 temp temp temp etc
etc etc etc etc
I've managed to make a dataframe with my dictionary with
df = pandas.DataFrame(weather)
but I end up with 1 row and a ton of columns.
I've checked the documentation for DataFrame and DataFrame.from_dict, but neither were very extensive nor provided many examples.
Given that "the keys are the day of the year... and the values are lists of temperatures", your method of construction should work. For example,
In [12]: weather = {'Jan 1':[1,2], 'Jan 2':[3,4]}
In [13]: df = pd.DataFrame(weather)
In [14]: df
Out[14]:
Jan 1 Jan 2
0 1 3
1 2 4