How does pandas treat equal values in the column it is sorting by.
dataFrame1
a | b | c |
--|---|---|
1 | 2 | 2 |
2 | 1 | 6 |
2 | 1 | 5 |
3 | 4 | 2 |
If I run dataFrame1.sort_values(by=['a'], ascending=True)
How does it treat the duplicate values in a ?
Related
I have a DataFrame that is similar to this one:
| | id | Group1 | Group2 | Group3 |
|---|----|--------|--------|--------|
| 0 | 22 | A | B | C |
| 1 | 23 | B | C | D |
| 2 | 24 | C | B | A |
| 3 | 25 | D | A | C |
And I want to get something like this:
| | Group | id_count |
|---|-------|----------|
| 0 | A | 3 |
| 1 | B | 3 |
| 2 | C | 3 |
| 3 | D | 2 |
Basically for each group I want to know how many people(id) have chosen it.
I know there is pd.groupby(), but it only gives an appropriate result for one column (if I give it a list, it does not combine group 1,2,3 in one column).
Use DataFrame.melt with GroupBy.size:
df1 = (df.melt('id', value_name='Group')
.groupby('Group')
.size()
.reset_index(name='id_count'))
print (df1)
Group id_count
0 A 3
1 B 3
2 C 4
3 D 2
I have the following data
---+--
b | c
--+---+--
a
--+---+--
0 | 1 | 2
1 | 3 | 4
I would like to drop the level of column a and have the following as output:
--+---+--
a| b | c
--+---+--
0 | 1 | 2
1 | 3 | 4
I have tried df.droplevel(1) but got the following error:
IndexError: Too many levels: Index has only 1 level, not 2
Any help is appreciated.
As suggested by #Alollz & #Ben, I did the following:
df = df.reset_index()
And got the following output
---+---+---+---+
| a | b | c
---+---+---+---+--
0 | 0 | 1 | 2
1 | 1 | 3 | 4
I have data like this:
+---+---+---+
| A | B | C |
+---+---+---+
| 1 | 2 | 7 |
| 2 | 2 | 7 |
| 3 | 2 | 1 |
| 3 | 2 | 1 |
| 3 | 2 | 1 |
+---+---+---+
Need to count unique value of each column and report it like below:
+---+---+---+
| A | 3 | 3 |
| A | 2 | 1 |
| A | 1 | 1 |
| B | 2 | 5 |
| C | 1 | 3 |
| C | 7 | 2 |
+---+---+---+
I have no issue when number of column are limit and manually name them, when input file is big it become hard,need to have simple way to have output
here is the code I have
import pandas as pd
df=pd.read_csv('1.csv')
A=df['A']
B=df['B']
C=df['C']
df1=A.value_counts()
df2=B.value_counts()
df3=C.value_counts()
all = {'A': df1,'B': df2,'C': df3}
result = pd.concat(all)
result.to_csv('out.csv')
Use DataFrame.stack with SeriesGroupBy.value_counts and then convert Series to DataFrame by Series.rename_axis and Series.reset_index and :
df=pd.read_csv('1.csv')
result = (df.stack()
.groupby(level=1)
.value_counts()
.rename_axis(['X','Y'])
.reset_index(name='Z'))
print (result)
X Y Z
0 A 3 3
1 A 1 1
2 A 2 1
3 B 2 5
4 C 1 3
5 C 7 2
X Y Z
2 A 3 3
0 A 1 1
1 A 2 1
3 B 2 5
4 C 1 3
5 C 7 2
result.to_csv('out.csv', index=False)
You can loop over column and insert them in dictionary.
you can initiate dictionary by all={}. To be scalable you can read column by colm=df.columns. This would give you all column in your df.
Try this code:
import pandas as pd
df=pd.read_csv('1.csv')
all={}
colm=df.columns
for i in colm:
all.update({i:df[i].value_counts()})
result = pd.concat(all)
result.to_csv('out.csv')
to find unique values of a data-frame.
df.A.unique()
to know the count of the unique values.
len(df.A.unique())
unique create an array to find the count use len() function
I have two Spark DataFrames, with values that I would like to add, and then multiply, and keep the lowest pair of values only. I have written a function that will do this:
math_func(aValOne, aValTwo, bValOne, bValTwo):
tmpOne = aValOne + bValOne
tmpTwo = aValTwo + bValTwo
final = tmpOne*tmpTwo
return final
I would like to iterate through two Spark DataFrames, "A" and "B", row by row, and keep the lowest values results. So if I have two DataFrames:
DataFrameA:
ID | ValOne | ValTwo
0 | 2 | 4
1 | 3 | 6
DataFrameB:
ID | ValOne | ValTwo
0 | 4 | 5
1 | 7 | 9
I would like to first take row 0 from DataFrameA:, compare it to rows 0 and 1 of DataFrameB, and then keep the lowest value results. I have tried this:
results = DataFrameA.select('ID')(lambda i: DataFrameA.select('ID')(math_func(DataFrameA.ValOne, DataFrameA.ValTwo, DataFrameB.ValOne, DataFrameB.ValOne))
but I get errors about iterating through a DataFrame column. I know that in Pandas I would essentially make a nested "for loop", and then just write the results to another DataFrame and append the results. The results I would expect are:
Initial Results:
DataFrameA_ID | Value | DataFrameB_ID
0 | 54 | 0
0 | 117 | 1
1 | 77 | 0
1 | 150 | 1
Final Results:
DataFrameA_ID | Value | DataFrameB_ID
0 | 54 | 0
1 | 77 | 0
I am quite new at Spark, but I know enough to know I'm not approaching this the right way.
Any thoughts on how to go about this?
You will need multiple steps to achieve this.
Suppose you have data
DFA:
ID | ValOne | ValTwo
0 | 2 | 4
1 | 3 | 6
DFB:
ID | ValOne | ValTwo
0 | 4 | 5
1 | 7 | 9
Step 1.
Do a cartesian join on your 2 dataframes. That will give you:
Cartesian:
DFA.ID | DFA.ValOne | DFA.ValTwo | DFB.ID | DFB.ValOne | DFB.ValTwo
0 | 2 | 4 | 0 | 4 | 5
1 | 3 | 6 | 0 | 4 | 5
0 | 2 | 4 | 1 | 7 | 9
1 | 3 | 6 | 1 | 7 | 9
Step 2.
Multiply columns:
Multiplied:
DFA.ID | DFA.Mul | DFB.ID | DFB.Mul
0 | 8 | 0 | 20
1 | 18 | 0 | 20
0 | 8 | 1 | 63
1 | 18 | 1 | 63
Step 3.
Group by DFA.ID and select min from DFA.Mul and DFB.Mul
So I have a dataframe similar to this:
timestamp | name
------------+------------
1 | a
1 | b
2 | c
2 | d
2 | e
3 | f
4 | g
Essentially I want to get min and max value of each timestamp session(defined by unique timestamp value, there are 4 sessions in this example), the expected result would something like this:
timestamp | name | start | end
------------+----------+--------+------
1 | a | 1 | 2
1 | b | 1 | 2
2 | c | 2 | 3
2 | d | 2 | 3
2 | e | 2 | 3
3 | f | 3 | 4
4 | g | 4 | 4
I am thinking index on timestamp column, then "move up" the index by 1, yet this approach didn't work on the forth bucket in the example above.
Any help is greatly appreciated!
try numpy.clip(), such as df['end']=numpy.clip(df['timestamp']+1, 0, 4)