How do I interpolate the nearest number?
My pd.Series named df1
0 RK
1 1
2 2
3 3
4 4
5 NaN
6 6
7 7
8 8
9 NaN
10 10
And I would like to interpolate the nearest number to replace NaN, like this
0 RK
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
According to Official Doc pandas.Series.interpolate
I tried
df1 = df1.interpolate(method='nearest',axis=0)
but it doesn't change.
Need help, and thanks in advance. :~)
just do
df1.interpolate()
don't bother with the method='nearest' option. The default method='linear' should do the trick.
Related
i need to compute quantiles for a large DF across columns or column-wise along rows or "months" in my case. Apparently, the quantile function applied on just a df works using the key word "axis" but if you try and apply quantile using a groupby, it is rejected with an error:
TypeError: quantile() got an unexpected keyword argument 'axis'
Here is the situation that the quantile works with data like this:
Num Num Num Quantile 0.5
5 6 4 5
4 1 2 2
3 9 7 7
7 2 8 7
5 5 4 5
But, if I add more columns with a groupby statement to find the same quantile(0.5, axis=1), then I get the error shown above. Please help and thank you. My actual data looks like this below:
site month Num Num Num Quantile 0.5
0 A 8 5 6 4 5
1 A 9 4 1 2 2
2 A 10 3 9 7 7
3 A 11 7 2 8 7
4 A 12 5 5 4 5
5 B 8 3 7 5 5
6 B 9 6 9 0 6
7 B 10 4 1 3 3
8 B 11 8 3 0 3
9 B 12 5 6 8 6
The confusion arises from the fact that pd.DataFrame.quantile and DataFrameGroupBy.quantile are not the same functions. The first one has an axis parameter, the second one does not. Hence the error.
When you think about it, it is perfectly logical that the second function does not have this option. Suppose we do:
groups = df.groupby('site')
for group in groups:
print(group[1])
site month Num Num.1 Num.2
0 A 8 5 6 4
1 A 9 4 1 2
2 A 10 3 9 7
3 A 11 7 2 8
4 A 12 5 5 4
site month Num Num.1 Num.2
5 B 8 3 7 5
6 B 9 6 9 0
7 B 10 4 1 3
8 B 11 8 3 0
9 B 12 5 6 8
Now ask yourself the question which axis could generate a qauntile that is meaningfully related to A | B. The answer surely is column-wise. I could get a quantile of Num for A, or Num.1. E.g.:
print(groups.quantile())
month Num Num.1 Num.2
site
A 10.0 5.0 5.0 4.0
B 10.0 5.0 6.0 3.0
It wouldn't make sense to say, let's get the quantile row-wise for A at row 0 (and pretend that this has anything to do with A as a grouped value as distinct from B). Indeed, you don't need a groupby for that at all.
Sidenote: you will have noticed that your columns Num, Num, Num have turned into Num, Num.1, Num.2 in my examples. This conversion takes place automatically when you read from the clipboard (pd.read_clipboard). In general, having multiple columns with duplicate names is very bad practice and might get you into all sorts of problems with various operators. So, I strongly advice you to rename them.
Here is an example code to demonstrate my problem:
import numpy as np
import pandas as pd
np.random.seed(10)
df = pd.DataFrame(np.random.randint(0,10,size=(100, 2)), columns=list('xy'))
df
x y
0 9 4
1 0 1
2 9 0
3 1 8
4 9 0
... ... ...
95 0 4
96 6 4
97 9 8
98 0 7
99 1 7
groups = df.groupby(['x'])
groups.size()
x
0 11
1 12
2 15
3 13
4 14
5 5
6 6
7 9
8 5
9 10
dtype: int64
How can I access the x-values as a column and the aggregated y-values as a second column to plot x versus y?
Two options.
Use reset_index():
groups = df.groupby(['x']).size().reset_index(name='size')
Add as_index=False to groupby:
groups = df.groupby(['x'], as_index=False).size()
Output for both:
>>> groups
x size
0 0 16
1 1 9
2 2 9
3 3 5
4 4 7
5 5 10
6 6 10
7 7 7
8 8 12
9 9 15
IIUC, use as_index=False:
groups = df.groupby(['x'], as_index=False)
out = groups.size()
out.plot(x='x', y='size')
If you only want to plot, you can also keep the x as index:
df.groupby(['x']).size().plot()
output:
x size
0 0 16
1 1 9
2 2 9
3 3 5
4 4 7
5 5 10
6 6 10
7 7 7
8 8 12
9 9 15
I have a tabular data like this one.
1 4 7
2 5 8
3 6 9
I would like data that look like this
1
2
3
4
5
6
7
8
9
Does anyone know how to use pandas to do this. (or maybe the keyword for this methodology to search for since I don't know how to properly call the procedure.)
Thank you in advance!
You can use numpy reshaping and pandas DataFrame constructor:
pd.DataFrame(df.values.reshape(-1,1, order='F'))
Output:
0
0 1
1 2
2 3
3 4
4 5
5 6
6 7
7 8
8 9
I need to write a query that will get a name of the company and shows only 2 rows and after that will check the next company and shows another 2 rows. Let's say that df looks like the one below:
x y y name
1 2 3 ammazon
4 5 6 ammazon
7 8 9 ammazon
9 8 7 google
6 5 4 google
3 2 1 google
So result should be like that:
x y y name
1 2 3 ammazon
4 5 6 ammazon
9 8 7 google
6 5 4 google
I tried to use sql query but couldn't write the correct one. Could you help ? Or perhaps "for loop" would be better solution... anything
Thanks All !
groupby with head
df.groupby('name').head(2)
x y y name
0 1 2 3 ammazon
1 4 5 6 ammazon
3 9 8 7 google
4 6 5 4 google
I have the following data frame
val sum
0 1 0
1 2 0
2 3 0
3 4 0
4 5 0
5 6 0
6 7 0
I would like to calculate the sum of the next three rows' (including the current row) values. I need to do this for very big files. What is the most efficient way? The expected result is
val sum
0 1 6
1 2 9
2 3 12
3 4 15
4 5 18
5 6 13
6 7 7
In general, how can I dynamically referencing to other rows (via boolean operations) while making assignments?
> pd.rolling_sum(df['val'], window=3).shift(-2)
0 6
1 9
2 12
3 15
4 18
5 NaN
6 NaN
If you want the last values to be "filled in" then you'll need to tack on NaN's to the end of your dataframe.