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Is there a better way to group by a category, and then select values based on different column values in Pandas?

I have an issue where I want to group by a date column, sort by a time column, and grab the resulting values in the values column.
The data that looks something like this
time value date
0 12.850000 19.195359 08-22-2019
1 9.733333 13.519543 09-19-2019
2 14.083333 9.191413 08-26-2019
3 16.616667 18.346598 08-19-2019
...
Where every date can occur multiple times, recording values at different points
during the day.
I wanted to group by date, and extract the minimum and maximum values of those groupings so I did this:
dayMin = df.groupby('date').value.min()
which gives me a Series object that is fairly easy to manipulate. The issue
comes up when I want to group by 'date', sort by 'time', then grab the 'value'.
What I did was:
dayOpen = df.groupby('date').apply(lambda df: df[ df.time == df.time.min() ])['value']
which almost worked, resulting in a DataFrame of:
date
08-19-2019 13344 17.573522
08-20-2019 12798 19.496609
08-21-2019 2009 20.033917
08-22-2019 5231 19.393700
08-23-2019 12848 17.784213
08-26-2019 417 9.717627
08-27-2019 6318 7.630234
I figured out how to clean up those nasty indexes to the left, name the column, and even concat with my dayMin Series to achieve my goal.
Ultimately my question is if there is a nicer way to perform these data manipulations that follow the general pattern of: "Group by column A, perform filtering or sorting operation on column B, grab resulting values from column C" for future applications.
Thank you in advance :)

You can sort the data frame before calling groupby:
first_of_day = df.sort_values('time').groupby('date').head(1)

This should work for you:
df.sort_values('time').groupby(['date'])['value'].agg([('Min' , 'min'), ('Max', 'max')])
For this small example:
Result df:

Update main dataframe based on sub dataframes coming from groupby

I am pretty new to pandas and trying to learn it. So, any advice would be appreciated :)
This is just a small part of my whole dataframe DF2:
Chromosome_Name
Sequence_Source
Sequence_Feature
Start
End
Strand
Gene_ID
Gene_Name
0
1
ensembl_havana
gene
14363
34806
-
"ENSG00000227232"
"WASH7P"
1
1
havana
gene
89295
138566
-
"ENSG00000238009"
"RP11-34P13.7"
2
1
havana
gene
141474
178862
-
"ENSG00000241860"
"RP11-34P13.13"
3
1
havana
gene
227615
272253
-
"ENSG00000228463"
"AP006222.2"
4
1
ensembl_havana
gene
312720
453948
+
"ENSG00000237094"
"RP4-669L17.10"
These are my conditions:
Condition 1: Reference row's "Start" value <= Other row's "End" value.
Condition 2: Reference row's "End" value >= Other row's "Start" value.
This is what I have done so far:
chromosome_list = ["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","X","Y"]
dataFrame = DF2.groupby(["Chromosome_Name"])
for chromosome in chromosome_list:
CHR = dataFrame.get_group(chromosome)
for i in range(0, len(CHR)-1):
for j in range(i+1, len(CHR)):
Overlap_index = DF2[(DF2.loc[i, ["Chromosome_Name"] == chromosome]) & (DF2.loc[i, ["Start"]] <= DF2.loc[j, ["End"]]) & (DF2.loc[i, ["End"]] >= DF2.loc[j, ["Start"]])].index
DF2 = DF2.drop(Overlap_index )
The chromosome_list is all the unique values of column "Chromosome_Name".
Mainly, I want to check for each row that whether the columns ("Start" and "End") values are satisfying the conditions above. I believe I need to iterate a single row (reference row) over the particular rows found in the data frame. However, to achieve this I need to consider the value of the first column "Chromosome_Name".
More specifically, every row in DF2 should be checked according to the conditions stated above but, for example, a row at Chromosome_Name = 5 shouldn't be checked with the row of Chromosome_Name = 12. Therefore, first, I thought that I should split the dataframe using pd.groupby() according to Chromosome_Name then, using these dataframes' indexes, I could manipulate (drop the given rows from) the DF2. However, it did not work :)
P.S. After DF2 is splitted into sub dataframes (according to unique Chromosome_Name), each sub dataframe has different size. e.g. There are 641 rows at Chromosome_Name = X but there are 19342 rows for the Chromosome_Name = 1
If you know how to correct my code or provide me another solution, I would be glad.
Thanks in advance.

I am new to pandas too so I do not want to give you a wrong insight and advices but have you ever thougth of converting Start and End columns to lists. So that you can use if statement if you are not comfortable with pandas but your task is urgent. However, I am aware that converting dataframe into list would be something opposite to the creation of pandas.

Assign value to dataframe from another dataframe based on two conditions

I am trying to assign values from a column in df2['values'] to a column df1['values']. However values should only be assigned if:
df2['category'] is equal to the df1['category'] (rows are part of the same category)
df1['date'] is in df2['date_range'] (date is in a certain range for a specific category)
So far I have this code, which works, but is far from efficient, since it takes me two days to process the two dfs (df1 has ca. 700k rows).
for i in df1.category.unique():
for j in df2.category.unique():
if i == j: # matching categories
for ia, ra in df1.loc[df1['category'] == i].iterrows():
for ib, rb in df2.loc[df2['category'] == j].iterrows():
if df1['date'][ia] in df2['date_range'][ib]:
df1.loc[ia, 'values'] = rb['values']
break
I read that I should try to avoid using for-loops when working with dataframes. List comprehensions are great, however since I do not have a lot of experience yet, I struggle formulating more complicated code.
How can I iterate over this problem more efficient? What essential key aspect should I think about when iterating over dataframes with conditions?
The code above tends to skip some rows or assigns them wrongly, so I need to do a cleanup afterwards. And the biggest problem, that it is really slow.
Thank you.
Some df1 insight:
df1.head()
date category
0 2015-01-07 f2
1 2015-01-26 f2
2 2015-01-26 f2
3 2015-04-08 f2
4 2015-04-10 f2
Some df2 insight:
df2.date_range[0]
DatetimeIndex(['2011-11-02', '2011-11-03', '2011-11-04', '2011-11-05',
'2011-11-06', '2011-11-07', '2011-11-08', '2011-11-09',
'2011-11-10', '2011-11-11', '2011-11-12', '2011-11-13',
'2011-11-14', '2011-11-15', '2011-11-16', '2011-11-17',
'2011-11-18'],
dtype='datetime64[ns]', freq='D')
df2 other two columns:
df2[['values','category']].head()
values category
0 01 f1
1 02 f1
2 2.1 f1
3 2.2 f1
4 03 f1

Edit: Corrected erroneous code and added OP input from a comment
Alright so if you want to join the dataframes on similar categories, you can merge them :
import pandas as pd
df3 = df1.merge(df2, on = "category")
Next, since date is a timestamp and the "date_range" is actually generated from two columns, per OP's comment, we rather use :
mask = (df3["startdate"] <= df3["date"]) & (df3["date"] <= df3["enddate"])
subset = df3.loc[mask]
Now we get back to df1 and merge on the common dates while keeping all the values from df1. This will create NaN for the subset values where they didn't match with df1 in the earlier merge.
As such, we set df1["values"] where the entries in common are not NaN and we leave them be otherwise.
common_dates = df1.merge(subset, on = "date", how= "left") # keeping df1 values
df1["values"] = np.where(common_dates["values_y"].notna(),
common_dates["values_y"], df1["values"])
N.B : If more than one df1["date"] matches with the date range, you'll have to drop some values otherwise duplicates mess up the explanation.

You could accomplish the first point:
1. df2['category'] is equal to the df1['category']
with the use of a join.
You could then use a for loop for filtering the data poings from df1[date] inside the merged dataframe that are not contemplated in the df2[date_range]. Unfortunately I need more information about the content of df1[date] and df2[date_range] to write the code here that would exactly do that.

How do I groupby a dataframe based on values that are common to multiple columns?

I am trying to aggregate a dataframe based on values that are found in two columns. I am trying to aggregate the dataframe such that the rows that have some value X in either column A or column B are aggregated together.
More concretely, I am trying to do something like this. Let's say I have a dataframe gameStats:
awayTeam homeTeam awayGoals homeGoals
Chelsea Barca 1 2
R. Madrid Barca 2 5
Barca Valencia 2 2
Barca Sevilla 1 0
... and so on
I want to construct a dataframe such that among my rows I would have something like:
team goalsFor goalsAgainst
Barca 10 5
One obvious solution, since the set of unique elements is small, is something like this:
for team in teamList:
aggregateDf = gameStats[(gameStats['homeTeam'] == team) | (gameStats['awayTeam'] == team)]
# do other manipulations of the data then append it to a final dataframe
However, going through a loop seems less elegant. And since I have had this problem before with many unique identifiers, I was wondering if there was a way to do this without using a loop as that seems very inefficient to me.

The solution is 2 folds, first compute goals for each team when they are home and away, then combine them. Something like:
goals_when_away = gameStats.groupby(['awayTeam'])['awayGoals', 'homeGoals'].agg('sum').reset_index().sort_values('awayTeam')
goals_when_home = gameStats.groupby(['homeTeam'])['homeGoals', 'awayGoals'].agg('sum').reset_index().sort_values('homeTeam')
then combine them
np_result = goals_when_away.iloc[:, 1:].values + goals_when_home.iloc[:, 1:].values
pd_result = pd.DataFrame(np_result, columns=['goal_for', 'goal_against'])
result = pd.concat([goals_when_away.iloc[:, :1], pd_result], axis=1, ignore_index=True)
Note .values when summing to get result in numpy array, and ignore_index=True when concat, these are to avoid pandas trap when it sums by column and index names.

how to unstack a pandas dataframe with two sets of variables

I have a table that looks like this. Read from a CSV file, so no levels, no fancy indices, etc.
ID date1 amount1 date2 amount2
x 15/1/2015 100 15/1/2016 80
The actual file I have goes up to date5 and amount 5.
How can I convert it to:
ID date amount
x 15/1/2015 100
x 15/1/2016 80
If I only had one variable, I would use pandas.melt(), but with two variables I really don't know how to do it quickly.
I could do it manually exporting to a sqlite3 database in memory, and doing a union. Doing unions in pandas is more annoying because, unlike, SQL, it requires all field names to be the same, so in pandas I'd have to create a temporary dataframe and rename all the fields: a dataframe for date1 and amount1, rename the field to just date and amount, then do the same for all the other events, and only then can I do pandas.concat.
Any suggestions? Thanks!

Here is one way:
>>> pandas.concat(
... [pandas.melt(x, id_vars='ID', value_vars=x.columns[1::2].tolist(), value_name='date'),
... pandas.melt(x, value_vars=x.columns[2::2].tolist(), value_name='amount')
... ],
... axis=1
... ).drop('variable', axis=1)
ID date amount
0 x 15/1/2015 100
1 x 15/1/2016 80
The idea is to do two melts, one for each set of columns, then concat them. This assumes that the two kinds of columns are in alternating order, so that the columns[1::2] and columns[2::2] select them correctly. If not, you'd have to modify that part of it to choose the columns you want.
You can also do it with the little-known lreshape:
>>> pandas.lreshape(x, {'date': x.columns[1::2], 'amount': x.columns[2::2]})
ID date amount
0 x 15/1/2015 100
1 x 15/1/2016 80
However, lreshape is not really documented and it's not clear if it's supposed to be used.

If I assume that the columns always repeat, a simple trick provides the solution you want.
The trick lies in making a list of lists of the columns that go together, then looping over the main list appending as necessary. It does involve a call to pd.DataFrame() each time the loop runs. I am kind of pressed for time right now to find a way to avoid that. But it does work like you would expect it to, and for a small file, you should not have any problems (that is, run time).
In [1]: columns = [['date1', 'amount1'], ['date2', 'amount2'], ...]
In [2]: df_clean = pd.DataFrame(columns=['date', 'amount'])
for cols in columns:
df_clean = df_clean.append(pd.DataFrame(df.loc[:,cols].values,
columns=['date', 'amount']),
ignore_index=True)
df_clean
Out[2]: date amount
0 15/1/2015 100
1 15/1/2016 80
The neat thing about this is that it only runs over the DataFrame once, picking all the rows under the columns it is looping over. So if you have 5 column pairs, with 'n' rows under it, the loop will only run 5 times. For each run, it will append all 'n' rows below the columns to the clean DataFrame to give you a consistent result. You can then eliminate any NaN values and sort by date, or do whatever you want to do with the clean DF.
What do you think, does this beat creating an in-memory sqlite3 database?