I have a pandas DataFrame that looks like this with 12 clusters in total. Certain clusters don't appear in a certain season.
I want to create a multi-line graph over the seasons of the percent of a specific cluster over each season. So if there are 30 teams in the 97-98 season and there are 10 teams in Cluster 1, then that value would be .33 since cluster 1 has one third of the total possible spots.
It'll look like this
And I want the dateset to look like this, where each cluster has its own percentage of the whole number of clusters in that season by percentage. I've tried using pandas groupby method to get a bunch of lists and then use value_counts() on that but that doesn't work since looping through df.groupby(['SEASON']) returns tuples, not a Series..
Thanks so much
Use .groupby combined with .value_counts and .unstack:
temp_df = df.groupby(['SEASON'])['Cluster'].value_counts(normalize=True).unstack().fillna(0.0)
temp_df.plot()
print(temp_df.round(2))
Cluster 0 1 2 4 5 6 7 10 11
SEASON
1996-97 0.1 0.21 0.17 0.21 0.07 0.1 0.03 0.07 0.03
1997-98 0.2 0.00 0.20 0.20 0.00 0.0 0.20 0.20 0.00
Related
I have a dataframe like below.
time speed
0 1 0.20
1 2 0.40
2 3 2.00
3 4 3.00
4 5 0.40
5 6 0.43
6 7 6.00
I would like to find the first occurance of a number ( in 'Speed' Column) that is closest to an input value I enter.
For example :
input value = 0.43
Expected Output :
Speed : 0.40 & corresponding Time : 2
The speed column should not be sorted for this problem.
I tried the below,but not getting the expected output.
Any help on this would be appreciated.
absolute closest
You can compute the absolute difference to your reference and get the idxmin:
speed_input = 0.43
df.loc[abs(df['speed']-speed_input).idxmin()]
output:
time 6.00
speed 0.43
Name: 5, dtype: float64
first closest with threshold:
i = 0.43
thresh = 0.03
df.loc[abs(df['speed']-i).le(thresh).idxmax()]
output:
time 2.0
speed 0.4
Name: 1, dtype: float64
One idea is round both values:
df[[(df['speed'].round(1)-round(speed_input, 1)).abs().idxmin()]]
I have a dataset like below
data = {'ReportingDate':['2013/5/31','2013/5/31','2013/5/31','2013/5/31','2013/5/31','2013/5/31',
'2013/6/28','2013/6/28',
'2013/6/28','2013/6/28','2013/6/28'],
'MarketCap':[' ',0.35,0.7,0.875,0.7,0.35,' ',1,1.5,0.75,1.25],
'AUM':[3.5,3.5,3.5,3.5,3.5,3.5,5,5,5,5,5],
'weight':[' ',0.1,0.2,0.25,0.2,0.1,' ',0.2,0.3,0.15,0.25]}
# Create DataFrame
df = pd.DataFrame(data)
df.set_index('Reporting Date',inplace=True)
df
Just a sample of a 8000 rows dataset.
ReportingDate starts from 2013/5/31 to 2015/10/30.
It includes data of all the months during the above period. But Only the last day of each month.
The first line of each month has two missing data. I know that
the sum of weight for each month is equal to 1
weight*AUM is equal to MarketCap
I can use the below line to get the answer I want, for only one month
a= (1-df["2013-5"].iloc[1:]['weight'].sum())
b= a* AUM
df.iloc[1,0]=b
df.iloc[1,2]=a
How can I use a loop to get the data for the whole period? Thanks
One way using pandas.DataFrame.groupby:
# If whitespaces are indeed whitespaces, not nan
df = df.replace("\s+", np.nan, regex=True)
# If not already datatime series
df.index = pd.to_datetime(df.index)
s = df["weight"].fillna(1) - df.groupby(df.index.date)["weight"].transform(sum)
df["weight"] = df["weight"].fillna(s)
df["MarketCap"] = df["MarketCap"].fillna(s * df["AUM"])
Note: This assumes that dates are always only the last day so that it is equivalent to grouping by year-month. If not so, try:
s = df["weight"].fillna(1) - df.groupby(df.index.strftime("%Y%m"))["weight"].transform(sum)
Output:
MarketCap AUM weight
ReportingDate
2013-05-31 0.350 3.5 0.10
2013-05-31 0.525 3.5 0.15
2013-05-31 0.700 3.5 0.20
2013-05-31 0.875 3.5 0.25
2013-05-31 0.700 3.5 0.20
2013-05-31 0.350 3.5 0.10
2013-06-28 0.500 5.0 0.10
2013-06-28 1.000 5.0 0.20
2013-06-28 1.500 5.0 0.30
2013-06-28 0.750 5.0 0.15
2013-06-28 1.250 5.0 0.25
I am trying to create a row in my existing pandas dataframe and the value of a new row should be a computation
I have a dataframe that looks like the below:
Rating LE_St % Total
1.00 7.58 74.55
2.00 0.56 5.55
3.00 0.21 2.04
5.00 0.05 0.44
6.00 1.77 17.42
All 10.17 100.00
I want to add a row called "Metric" which is the sum of "LE_St" variable for "Rating" >= 4 and <6 divided by "LE_St" for "All" i.e Metric = (0.05+1.77)/10.17
My output dataframe should look like below:
Rating LE_St % Total
1.00 7.58 74.55
2.00 0.56 5.55
3.00 0.21 2.04
5.00 0.05 0.44
6.00 1.77 17.42
All 10.17 100.00
Metric 0.44
I believe your approach to the dataframe is wrong.
Usually rows hold values correlating with columns in a matter that makes sense and not hold random information. the power of pandas and python is for holding and manipulating data. You can easily compute a value from a column or even all columns and store them in a "summary" like dataframe or in separate values. That might help you with this as well.
for computation on a column (i.e. Series object) you can use the .sum() method (or any other of the computational tools) and slice your dataframe by values in the "rating" column.
for random computation of small statistics you will be rather off with excel :)
an example of a solution might look like this:
all = 10.17 # i dont know where this value comes from
df = df[df['rating'].between(4, 6, inclusive=True)]
metric = sliced_df['LE_ST'].sum()/all
print metric # or store it somewhere however you like
I have a df as below. Consider df is indexed by timestamps as dtype='datetime64[ns]' i.e. 1970-01-01 00:00:27.603046999. I am putting dummy timestamps here.
Timestamp Address Type Arrival_Time Time_Delta
0.1 2 A 0.25 0.15
0.4 3 B 0.43 0.03
0.9 1 B 1.20 0.20
1.3 1 A 1.39 0.09
1.5 3 A 1.64 0.14
1.7 3 B 1.87 0.17
2.0 3 A 2.09 0.09
2.1 1 B 2.44 0.34
I have three unique "addresses" (1, 2,3).
I have two unique "types" (A, B)
Now what I am trying to do two things in simple way (possibly using pd.Grouper and pd.Groupby functions in Panda).
I want to group rows by fixed bin of 1 duration (using timestamp values). Then in each 1sec bin, for each "address" find the mean and sum of "Time_delta" only if "Type" = A.
I want to group rows by fixed bin of 1 duration (using timestamp values). Then in each bin, for each "address", find the mean and sum of Inter-Arrival Time*.
IAT = Arrival Time (i) - Arrival Time (i-1)
Note: If the timestamps duration/length is of 100 seconds, we should have exactly 100 rows in the output dataframe and six columns i.e. two (mean, sum) for each address.
For Problem 1:
I tried the following code:
df = pd.DataFrame({'Timestamp': Timestamp, 'Address': Address,
'Type': Type, 'Arrival_Time': Arrival_time, 'Time_Delta': Time_delta})
# Set index to Datetime
index = pd.DatetimeIndex(df[df.columns[3]]*10**9) # Convert timestamp into format
df = df.set_index(index) # Set timestamp as index
df_1 = df[df.columns[2]].groupby([pd.TimeGrouper('1S'), df['Address']]).mean().unstack(fill_value=0)
which gives results:
Timestamp 1 2 3
1970-01-01 00:00:00 0.20 0.15 0.030
1970-01-01 00:00:01 0.09 0.00 0.155
1970-01-01 00:00:02 0.34 0.00 0.090
As you can see, it gives the mean Time_delta for each address in the 1S bin, But I want to add the second condition i.e. find mean for each address only if Type=A. I hope problem 1 is now clear.
For Problem 2:
Its a bit complicated. I want to do get Mean IAT for each address in the same format (See below):
One possible way is to add an extra column to original df as df['IAT'], where
for in range (1, len(df))
i = 0
df['IAT'] = df['Arrival_Time'][i] - df['Arrival_Time'][i-1] i =
i=i+1
Then apply the same above code to find mean of IAT for each address if Type=A.
Actual Data
Timestamp Address Type Time Delta Arrival Time
1970-01-01 00:00:00.000000000 28:5a:ec:16:00:22 Control frame 0.000000 Nov 10, 2017 22:39:20.538561000
1970-01-01 00:00:00.000287000 28:5a:ec:16:00:23 Data frame 0.000287 Nov 10, 2017 22:39:20.548121000
1970-01-01 00:00:00.000896000 28:5a:ec:16:00:22 Control frame 0.000609 Nov 10, 2017 22:39:20.611256000
1970-01-01 00:00:00.001388000 28:5a:ec:16:00:21 Data frame 0.000492 Nov 10, 2017 22:39:20.321745000
... ...
I have data that consist of 1,000 samples from a distribution of a rate for several different countries stored in a pandas DataFrame:
s1 s2 ... s1000 pop
region country
NA USA 0.25 0.27 0.23 300
CAN 0.16 0.14 0.13 35
LA MEX ...
I need to multiply each sample by the population.To accomplish this, I currently have:
for column in data.filter(regex='sample'):
data[column] = data[column]*data['pop']
While this works, iterating over columns feels like it goes against the spirit of python and numpy. Is there a more natural way I'm not seeing? I would normally use apply, but I don't know how to use apply and still get the unique population value for each row.
More context: The reason I need to do this multiplication is because I want to aggregate the data by region, collapsing USA and CAN into North America, for example. However, because my data are rates, I cannot simply add- I must multiply by population to turn them into counts.
I might do something like
>>> df
s1 s2 s1000 pop
region country
NaN USA 0.25 0.27 0.23 300
CAN 0.16 0.14 0.13 35
[2 rows x 4 columns]
>>> df.iloc[:,:-1] = df.iloc[:, :-1].mul(df["pop"], axis=0)
>>> df
s1 s2 s1000 pop
region country
NaN USA 75.0 81.0 69.00 300
CAN 5.6 4.9 4.55 35
[2 rows x 4 columns]
where instead of iloc-ing every column except the last you could use any other loc-based filter.