I have 10 Hz data that has some values offset by 1 80 Hz frame. This means not all rows have the same number of terms. I'm trying to read this into a dataframe using read_table. Pandas complains that the rows aren't even. A sample of the data looks like this:
SGMT Foo Bar Baz Qux
2010/056/12:25:32.100 2.16123839150863E-03 1.95636410755160E+00
2010/056/12:25:32.112 -9.9458 6.063645E+2
2010/056/12:25:32.200 2.16123839150863E-03 1.95636410755160E+00
2012/056/12:25:32.212 -9.9452 6.059189E+2
2010/056/12:25:32.300 2.16123839150863E-03 1.95636410755160E+00
In reality, there are 36 columns of data on the even 10 hz marks and 6 on the offset ones.
My attempt at reading the code looks like this:
env_values = pd.read_table(filen, sep ='[\t ]*',index_col='SGMT', \
parse_dates='SGMT', date_parser=time_convert)
and the function time_covert is
def time_convert(tstr):
return pd.to_datetime(tstr, format='%Y/%j/%H:%M:%S.%f')
I want all the data to appear as if it happened on the 10 Hz boundry (0.100, 0.200) mark, and be one row in the pandas dataframe.
Can read_table do this or do I have to write a preprocessor to time align the data before giving it to pd.read_table?
Related
Today my problem is this: I have a dataframe of 300 X 41. Its encoded with numbers. I want to append an 'a' to each value in the dataframe so that another down stream program will not fuss about these being 'continuous variables' which they arent, they are factors. Simple right?
Every way I can think to do this though returns a dataframe or object that is not 300x 41...but just one long list of altered values:
Please end this headache for me. How can I do this in a way that returns a 400 X 31 altered output?
> dim(x)
[1] 300 41
>x2 <- sub("^","a",x)
>dim(x2)
[1] 12300 1
Python newbie here. Here's a simplified example of my problem. I have 2 pandas dataframes.
One dataframe lightbulb_df has data on whether a light is on or off and looks something like this:
Light_Time
Light On?
5790.76
0
5790.76
0
5790.771
1
5790.779
1
5790.779
1
5790.782
0
5790.783
1
5790.783
1
5790.784
0
Where the time is in seconds since start of day and 1 is the lightbulb is on, 0 means the lightbulb is off.
The second dataframe sensor_df shows whether or not a sensor detected the lightbulb and has different time values and rates.
Sensor_Time
Sensor Detect?
5790.8
0
5790.9
0
5791.0
1
5791.1
1
5791.2
1
5791.3
0
Both dataframes are very large with 100,000s of rows. The lightbulb will turn on for a few minutes and then turn off, then back on, etc.
Using the .diff function, I was able to compare each row to its predecessor and depending on whether the result was 1 or -1 create a truth table with simplified on and off times and append it to lightbulb_df.
# use .diff() to compare each row to the last row
lightbulb_df['light_diff'] = lightbulb_df['Light On?'].diff()
# the light on start times are when
#.diff is less than 0 (0 - 1 = -1)
light_start = lightbulb_df.loc[lightbulb_df['light_diff'] < 0]
# the light off start times (first times when light turns off)
# are when .diff is greater than 0 (1 - 0 = 1)
light_off = lightbulb_df.loc[lightbulb_df['light_diff'] > 0]
# and then I can concatenate them to have
# a single changed state df that only captures when the lightbulb changes
lightbulb_changes = pd.concat((light_start, light_off)).sort_values(by=['Light_Time'])
So I end up with a dataframe of on start times, a dataframe of off start times, and a change state dataframe that looks like this.
Light_Time
Light On?
light_diff
5790.771
1
1
5790.782
0
-1
5790.783
1
1
5790.784
0
-1
Now my goal is to search the sensor_df dataframe during each of the changed state times (above 5790.771 to 5790.782 and 5790.783 to 5790.784) by 1 second intervals to see whether or not the sensor detected the lightbulb. So I want to end up with the number of seconds the lightbulb was on and the number of seconds the sensor detected the lightbulb for each of the many light on periods in the change state dataframe. I'm trying to get % correctly detected.
Whenever I try to plan this out, I end up using lots of nested for loops or while loops which I know will be really slow with 100,000s of rows of data. I thought about using the .cut function to divide up the dataframe into 1 second intervals. I made a for loop to cycle through each of the times in the changed state dataframe and then nested a while loop inside to loop through 1 second intervals but that seems like it would be really slow.
I know python has a lot of built in functions that could help but I'm having trouble knowing what to google to find the right one.
Any advice would be appreciated.
Lets say i have Dataframe, which has 200 values, prices for products. I want to run some operation on this dataframe, like calculate average price for last 10 prices.
The way i understand it, right now pandas will go through every single row and calculate average for each row. Ie first 9 rows will be Nan, then from 10-200, it would calculate average for each row.
My issue is that i need to do a lot of these calculations and performance is an issue. For that reason, i would want to run the average only on say on last 10 values (dont need more) from all values, while i want to keep those values in the dataframe. Ie i dont want to get rid of those values or create new Dataframe.
I just essentially want to do calculation on less data, so it is faster.
Is something like that possible? Hopefully the question is clear.
Building off Chicodelarose's answer, you can achieve this in a more "pandas-like" syntax.
Defining your df as follows, we get 200 prices up to within [0, 1000).
df = pd.DataFrame((np.random.rand(200) * 1000.).round(decimals=2), columns=["price"])
The bit you're looking for, though, would the following:
def add10(n: float) -> float:
"""An exceptionally simple function to demonstrate you can set
values, too.
"""
return n + 10
df["price"].iloc[-12:] = df["price"].iloc[-12:].apply(add10)
Of course, you can also use these selections to return something else without setting values, too.
>>> df["price"].iloc[-12:].mean().round(decimals=2)
309.63 # this will, of course, be different as we're using random numbers
The primary justification for this approach lies in the use of pandas tooling. Say you want to operate over a subset of your data with multiple columns, you simply need to adjust your .apply(...) to contain an axis parameter, as follows: .apply(fn, axis=1).
This becomes much more readable the longer you spend in pandas. 🙂
Given a dataframe like the following:
Price
0 197.45
1 59.30
2 131.63
3 127.22
4 35.22
.. ...
195 73.05
196 47.73
197 107.58
198 162.31
199 195.02
[200 rows x 1 columns]
Call the following to obtain the mean over the last n rows of the dataframe:
def mean_over_n_last_rows(df, n, colname):
return df.iloc[-n:][colname].mean().round(decimals=2)
print(mean_over_n_last_rows(df, 2, "Price"))
Output:
178.67
I am using data collected from two different instruments which have different resolution because of the sampling rate of each instrument. For a specific time, one of the sets have >10k entries while the other has ~2.5k. They however capture data over the same time interval, and I want to plot them on top of each other even though they have different resolution in data. The minimum and maximum x of both sets are the same however one of them have more entries.
Simplified it could look like this:
1st set from instrument with higher sampling rate:
time(s) value
0.0 10
0.2 11
0.4 12
0.6 13
0.8 14
... ..
100 50
2nd set from instrument with lower sampling rate:
time(s) value
0 100
1 120
2 125
3 128
4 130
. ...
100 430
They are measuring different things, but I would like to display them in the same plot. How can I accomplish this?
I found the mistake.. I was trying to plot both datasets using the time data from the first instrument. Of course they need to be plotted with their respective time data and I put the first time data in the second plot by mistake..
I have a dataset that I shaped according to my needs, the dataframe is as follows:
Index A B C D ..... Z
Date/Time 1 0 0 0,35 ... 1
Date/Time 0,75 1 1 1 1
The total number of rows is 8878
What I try to do is create a time-series dendrogram (Example: Whole A column will be compared to whole B column in whole time).
I am expecting an output like this:
(source: rsc.org)
I tried to construct the linkage matrix with Z = hierarchy.linkage(X, 'ward')
However, when I print the dendrogram, it just shows an empty picture.
There is no problem if a compare every time point with each other and plot, but in that way, the dendrogram becomes way too complicated to observe even in truncated form.
Is there a way to handle the data as a whole time series and compare within columns in SciPy?