Develop Reference

Ordering by multiple columns including Count in PySpark

I am currently working on understanding Pyspark, and am running into a problem. I am attempting to resolve how to order by multiple columns in the dataframe, when one of these is a count.
As an example, say I have a dataframe (df) with three columns, A,B,and C. I want to group by A and B, and then count these instances. So if there are 10 instances where A=1 and B=1, the Table for that row should look like:
A|B|Count
1 1 10
I have determined that I can do this fairly easily by running:
df.groupBy('A', 'B').count()
Then if I want to order this dataframe by count (descending), this is also pretty straightforward:
df.groupBy('A', 'B').count().orderBy(desc("count"))
This next step is where I am having trouble. What if now I want to also order by column C, ie order first by count, and then by C? I had thought that the syntax would be something akin to:
df.groupBy('A', 'B').count().orderBy(desc("count"), desc("C"))
But this does not work, presumably because once I run count(), the dataframe is limited to only the columns A, B, and count. Do I need to somehow create a new column in the original dataframe with the count column, and if so, how can I do this?
Is there another simpler way that I am missing to order by both count and C?
For clarity an example dataframe that I would like to end with could appear as:
A|B|Count|C
1 1 10 5
1 2 9 3
1 5 9 1
2 4 8 10
2 7 8 5
Any insights or guidance are greatly appreciated.

Try using a window Function , the column 'C' is not in the group by, hence is not available for order/sorting the columns. If you just want the grouped columns eg A,B and the count column, you can always use select statement to get just that after the window function.
from pyspark.sql.window import Window
windowSpec = Window.partitionBy("A","B")
df.withColumn('count',F.count('*').over(windowSpec)).select("A","B","count").distinct().orderBy(F.col('count').desc(),F.col('C').desc()).show()

Select rows from Dataframe with variable number of conditions

I'm trying to write a function that takes as inputs a DataFrame with a column 'timestamp' and a list of tuples. Every tuple will contain a beginning and end time.
What I want to do is to "split" the dataframe in two new ones, where the first contains the rows for which the timestamp value is not contained between the extremes of any tuple, and the other is just the complementary.
The number of filter tuples is not known a priori though.
df = DataFrame({'timestamp':[0,1,2,5,6,7,11,22,33,100], 'x':[1,2,3,4,5,6,7,8,9,1])
filt = [(1,4), (10,40)]
left, removed = func(df, filt)
This should give me two dataframes
left: with rows with timestamp [0,5,6,7,100]
removed: with rows with timestamp [1,2,11,22,33]
I believe the right approach is to write a custom function that can be used as a filter, and then call is somehow to filter/mask the dataframe, but I could not find a proper example of how to implement this.

Check
out = df[~pd.concat([df.timestamp.between(*x) for x in filt]).any(level=0)]
Out[175]:
timestamp x
0 0 1
3 5 4
4 6 5
5 7 6
9 100 1

Can't you use filtering with .isin():
left,removed = df[df['timestamp'].isin([0,5,6,7,100])],df[df['timestamp'].isin([1,2,11,22,33])]

Why does groupby operations behave differently

When using pandas groupby functions and manipulating the output after the groupby, I've noticed that some functions behave differently in terms of what is returned as the index and how this can be manipulated.
Say we have a dataframe with the following information:
Name Type ID
0 Book1 ebook 1
1 Book2 paper 2
2 Book3 paper 3
3 Book1 ebook 1
4 Book2 paper 2
if we do
df.groupby(["Name", "Type"]).sum()
we get a DataFrame:
ID
Name Type
Book1 ebook 2
Book2 paper 4
Book3 paper 3
which contains a MultiIndex with the columns used in the groupby:
MultiIndex([('Book1', 'ebook'),
('Book2', 'paper'),
('Book3', 'paper')],
names=['Name', 'Type'])
and one column called ID.
but if I apply a size() function, the result is a Series:
Name Type
Book1 ebook 2
Book2 paper 2
Book3 paper 1
dtype: int64
And at last, if I do a pct_change(), we get only the resulting DataFrame column:
ID
0 NaN
1 NaN
2 NaN
3 0.0
4 0.0
TL;DR. I want to know why some functions return a Series whilst some others a DataFrame as this made me confused when dealing with different operations within the same DataFrame.

From the document
Size:
Returns
Series
Number of rows in each group.
For the sum , since you did not pass the column for sum, so it will return the data frame without the groupby key
df.groupby(["Name", "Type"])['ID'].sum() # return Series
Function like diff and pct_change is not agg, it will return the value with the same index as original dataframe, for count , mean, sum they are agg, return with the value and groupby key as index

The outputs are different because the aggregations are different, and those are what mostly control what is returned. Think of the array equivalent. The data are the same but one "aggregation" returns a single scalar value, the other returns an array the same size as the input
import numpy as np
np.array([1,2,3]).sum()
#6
np.array([1,2,3]).cumsum()
#array([1, 3, 6], dtype=int32)
The same thing goes for aggregations of a DataFrameGroupBy object. All the first part of the groupby does is create a mapping from the DataFrame to the groups. Since this doesn't really do anything there's no reason why the same groupby with a different operation needs to return the same type of output (see above).
gp = df.groupby(["Name", "Type"])
# Haven't done any aggregations yet...
The other important part here is that we have a DataFrameGroupBy object. There are also SeriesGroupBy objects, and that difference can change the return.
gp
#<pandas.core.groupby.generic.DataFrameGroupBy object>
So what happens when you aggregate?
With a DataFrameGroupBy when you choose an aggregation (like sum) that collapses to a single value per group the return will be a DataFrame where the indices are the unique grouping keys. The return is a DataFrame because we provided a DataFrameGroupBy object. DataFrames can have multiple columns and had there been another numeric column it would have aggregated that too, necessitating the DataFrame output.
gp.sum()
# ID
#Name Type
#Book1 ebook 2
#Book2 paper 4
#Book3 paper 3
On the other hand if you use a SeriesGroupBy object (select a single column with []) then you'll get a Series back, again with the index of unique group keys.
df.groupby(["Name", "Type"])['ID'].sum()
|------- SeriesGroupBy ----------|
#Name Type
#Book1 ebook 2
#Book2 paper 4
#Book3 paper 3
#Name: ID, dtype: int64
For aggregations that return arrays (like cumsum, pct_change) a DataFrameGroupBy will return a DataFrame and a SeriesGroupBy will return a Series. But the index is no longer the unique group keys. This is because that would make little sense; typically you'd want to do a calculation within the group and then assign the result back to the original DataFrame. As a result the return is indexed like the original DataFrame you provided for aggregation. This makes creating these columns very simple as pandas handles all of the alignment
df['ID_pct_change'] = gp.pct_change()
# Name Type ID ID_pct_change
#0 Book1 ebook 1 NaN
#1 Book2 paper 2 NaN
#2 Book3 paper 3 NaN
#3 Book1 ebook 1 0.0 # Calculated from row 0 and aligned.
#4 Book2 paper 2 0.0
But what about size? That one is a bit weird. The size of a group is a scalar. It doesn't matter how many columns the group has or whether values in those columns are missing, so sending it a DataFrameGroupBy or SeriesGroupBy object is irrelevant. As a result pandas will always return a Series. Again being a group level aggregation that returns a scalar it makes sense to have the return indexed by the unique group keys.
gp.size()
#Name Type
#Book1 ebook 2
#Book2 paper 2
#Book3 paper 1
#dtype: int64
Finally for completeness, though aggregations like sum return a single scalar value it can often be useful to bring those values back to the every row for that group in the original DataFrame. However the return of a normal .sum has a different index, so it won't align. You could merge the values back on the unique keys, but pandas provides the ability to transform these aggregations. Since the intent here is to bring it back to the original DataFrame, the Series/DataFrame is indexed like the original input
gp.transform('sum')
# ID
#0 2 # Row 0 is Book1 ebook which has a group sum of 2
#1 4
#2 3
#3 2 # Row 3 is also Book1 ebook which has a group sum of 2
#4 4

Finding rows with highest means in dataframe

I am trying to find the rows, in a very large dataframe, with the highest mean.
Reason: I scan something with laser trackers and used a "higher" point as reference to where the scan starts. I am trying to find the object placed, through out my data.
I have calculated the mean of each row with:
base = df.mean(axis=1)
base.columns = ['index','Mean']
Here is an example of the mean for each row:
0 4.407498
1 4.463597
2 4.611886
3 4.710751
4 4.742491
5 4.580945
This seems to work fine, except that it adds an index column, and gives out columns with an index of type float64.
I then tried this to locate the rows with highest mean:
moy = base.loc[base.reset_index().groupby(['index'])['Mean'].idxmax()]
This gives out tis :
index Mean
0 0 4.407498
1 1 4.463597
2 2 4.611886
3 3 4.710751
4 4 4.742491
5 5 4.580945
But it only re-index (I have now 3 columns instead of two) and does nothing else. It still shows all rows.

Here is one way without using groupby
moy=base.sort_values('Mean').tail(1)

It looks as though your data is a string or single column with a space in between your two numbers. Suggest splitting the column into two and/or using something similar to below to set the index to your specific column of interest.
import pandas as pd
df = pd.read_csv('testdata.txt', names=["Index", "Mean"], delimiter="\s+")
df = df.set_index("Index")
print(df)

In pandas, how to plot with multiple index?

I have double index in Panda's dataframe like the example below.
c d
a b
1 3 1.519970 -0.493662
2 4 0.600178 0.274230
3 5 0.132885 -0.023688
4 6 2.410179 1.450520
How do I plot column 'c' as y-axis and index 'b' as x-axis. With one index, it is easy to plot. But I have trouble with multi index plotting. Thank you for any help!!.

Option 1
Two options have been provided (in the comments) involving reset_index. They are
df.reset_index().plot(x="b",y="c")
Or,
df.reset_index(level=0, drop=True).c.plot()
Both of these should work as expected, but will become expensive for large dataframes.
Option 2
If you are worried about memory, here's an option that does not involve resetting the index:
plt.plot(df.index.get_level_values(1), df.c)
reset_index generates copies of the data. This is more efficient, because it doesn't have to.