I am using pandas/python and I have two date time series s1 and s2, that have been generated using the ‘to_datetime’ function on a field of the df containing dates/times.
When I subtract s1 from s2
s3 = s2 – s1
I get a series, s3, of type
timedelta64[ns]
0 385 days, 04:10:36
1 57 days, 22:54:00
2 642 days, 21:15:23
3 615 days, 00….
I currently have a dataframe consisting of columns with 1’s and 0’s as values, I would like to iterate through the columns and delete the ones that are made up of only 0’s. Here’s what I have tried so far:
ones = []
zeros = []
for year in years:
for i in range(0,599):
if year[str(i)].values.any() == 1:
ones.append(i)
….
I’m trying to test if one of my variables is pd.NaT. I know it is NaT, and still it won’t pass the test. As an example, the following code prints nothing :
a=pd.NaT
if a == pd.NaT:
print(“a not NaT”)
Does anyone have a clue ? Is there a way to effectively test if a is NaT?
….
I have a pandas dataframe and would like to plot values from one column versus the values from another column. Fortunately, there is plot method associated with the data-frames that seems to do what I need:
df.plot(x=’col_name_1′, y=’col_name_2′)
Unfortunately, it looks like among the plot styles (listed here after the kind parameter) there are n….
How to group values of pandas dataframe and select the latest(by date) from each group?
For example, given a dataframe sorted by date:
id product date
0 220 6647 2014-09-01
1 220 6647 2014-09-03
2 220 6647 2014-10-16
3 826 3380 2014-11-11
4 826 3380 2014-12-09
5 826 3380 2015-05-19….
I have a large amount of data in a collection in mongodb which I need to analyze. How do i import that data to pandas?
I am new to pandas and numpy.
EDIT:
The mongodb collection contains sensor values tagged with date and time. The sensor values are of float datatype.
Sample Data:
{
“_cls” : “SensorReport”,
“_id” : ObjectId(“515a963b78f6a035d9fa5….
Questions
How do I use pd.concat?
What is the levels argument for?
What is the keys argument for?
Are there a bunch of examples to help explain how to use all the arguments?
Pandas’ concat function is the Swiss Army knife of the merging utilities. The variety of situations in which it is useful are numerous. The existing documentation leaves ou….
I have a pandas dataframe df as illustrated below:
BrandName Specialty
A H
B I
ABC J
D K
AB L
I want to replace ‘ABC’ and ‘AB’ in column BrandName by ‘A’.
Can someone help with this?
….
I would like to see if a particular string exists in a particular column within my dataframe.
I’m getting the error
ValueError: The truth value of a Series is ambiguous. Use a.empty,
a.bool(), a.item(), a.any() or a.all().
import pandas as pd
BabyDataSet = [(‘Bob’, 968), (‘Jessica’, 155), (‘Mary’, 77), (‘John’, 578), (‘Mel’, 973)]
a = pd.DataF….
i have a small sample data:
import pandas as pd
df = {‘ID’: [3009, 129, 119, 120, 121, 122, 130, 3014, 266, 849, 174, 844],
‘V’: [‘IGHV7-B*01’, ‘IGHV7-B*01’, ‘IGHV6-A*01’, ‘GHV6-A*01’, ‘IGHV6-A*01’,
‘IGHV6-A*01’, ‘IGHV4-L*03’, ‘IGHV4-L*03’, ‘IGHV5-A*01’, ‘IGHV5-A*04’,
‘IGHV6-A*02′,’IGHV6-A*02’],
‘Prob’: [1, 1, 0.8, 0.8056, 0.9….