I have a dataframe that looks like this.
d = {'name': ["eric", "eric","eric", "sean","sean","sean"], 'values': [1, 5, 7, 4, 2, 5]}
df = pd.DataFrame(data=d)
And I am trying to add a new column called df['sum'] where it adds up the value of the previous row for each unique name in the df['name'] column, so that it would look like this:
name values sum
eric 1 1
eric 5 6
eric 4 10
sean 7 7
sean 2 9
sean 5 14
I tried using the below, but can't figure out how to get it to start over each time it gets to a new name.
for i in df['name']:
df['sum'] = df['values'].cumsum()
Use groupby().cumsum()
df.groupby('name').values.cumsum()
Related
I have this data frame available. It has a timestamp for start, a timestamp for end and a duration column.
start
end
duration
1
5
4
2
5
3
3
4
1
4
6
2
5
9
4
6
7
1
7
10
3
I'd like to add a column 'rolling_mean' to the dataframe that calculates a rolling average on all previous rows (ordered by start) with this condition: only previous rows can be used for mean calculation where the event has already ended (so end date should be equal to or lower than the start date of the row for which the rolling mean is being calculated). So for row number 4, the rolling_mean is 1 because we look at all previous rows and only the previous one fulfills the condition of the event having ended.
This is the dataframe I'd like to get with a Pandas rolling mean:
start
end
duration
rolling_mean
1
5
4
Nan
2
5
3
Nan
3
4
1
Nan
4
6
2
1
5
9
4
2.666667
6
7
1
2.500000
7
10
3
2.200000
Here is the code to reproduce my example:
d = [[1, 5],
[2, 5],
[3, 4],
[4, 6],
[5, 9],
[6, 7],
[7, 10]]
df = pd.DataFrame(d, columns=['start_time', 'end_time'])
df['duration'] = df.end_time - df.start_time
I've tried to merge the dataframe with itself to then filter out the irrelevant rows, but the data frame is too big to take this approach.
So I'm looking for a rolling mean but where I can specify the extra condition.
Does anyone have any ideas for this one?
A for loop will do the job:
rolling_mean = np.repeat(np.nan, len(df))
start, end, duration = df[["start_time", "end_time", "duration"]].to_numpy().T
for i in range(len(df)):
matches = duration[:i][end[:i] <= start[i]]
if matches.any():
rolling_mean[i] = matches.mean()
I wish to extract the list of values from one column in pandas how to extract the list of values from one column and then use those values to create additional columns based on number of values within the list.
My dataframe:
a = pd.DataFrame({"test":["","","","",[1,2,3,4,5,6,6],"","",[11,12,13,14,15,16,17]]})
Current output:
test
0
1
2
3
4 [1, 2, 3, 4, 5, 6, 6]
5
6
7 [11, 12, 13, 14, 15, 16, 17]
expected output:
example_1 example_2 example_3 example_4 example_5 example_6 example_7
0
1
2
3
4 1 2 3 4 5 6 6
5
6 11 12 13 14 15 16 17
lets say we expect it to have 7 values for each list. <- this is most of my current case so if I can set the limit then it will be a good one. Thank you.
This should be what you're looking for. I replaced the nan values with blank cells, but you can change that of course.
a = pd.DataFrame({"test":["","","","",[1,2,3,4,5,6,6],"","",[11,12,13,14,15,16,17]]})
ab = a.test.apply(pd.Series).fillna("")
ab.columns = ['example_' + str(i) for i in range(1, 8)]
Output:
Edit: using .add_prefix() as the other answer uses is prettier than setting the column names manually with a list comprehension.
Here's a one-liner:
(pd.DataFrame
(a.test.apply(pd.Series)
.fillna("")
.set_axis(range(1, a.test.str.len().max() + 1), axis=1)
.add_prefix("example_")
)
The set_axis is just to make the columns 1-indexed, if you don't mind 0-indexing you can leave it out.
This is a multipart problem. I have found solutions for each separate part, but when I try to combine these solutions, I don't get the outcome I want.
Let's say this is my dataframe:
df = pd.DataFrame(list(zip([1, 3, 6, 7, 7, 8, 4], [6, 7, 7, 9, 5, 3, 1])), columns = ['Values', 'Vals'])
df
Values Vals
0 1 6
1 3 7
2 6 7
3 7 9
4 7 5
5 8 3
6 4 1
Let's say I want to find the pattern [6, 7, 7] in the 'Values' column.
I can use a modified version of the second solution given here:
Pandas: How to find a particular pattern in a dataframe column?
pattern = [6, 7, 7]
pat_i = [df[i-len(pattern):i] # Get the index
for i in range(len(pattern), len(df)) # for each 3 consequent elements
if all(df['Values'][i-len(pattern):i] == pattern)] # if the pattern matched
pat_i
[ Values Vals
2 6 7
3 7 9
4 7 5]
The only way I've found to narrow this down to just index values is the following:
pat_i = [df.index[i-len(pattern):i] # Get the index
for i in range(len(pattern), len(df)) # for each 3 consequent elements
if all(df['Values'][i-len(pattern):i] == pattern)] # if the pattern matched
pat_i
[RangeIndex(start=2, stop=5, step=1)]
Once I've found the pattern, what I want to do, within the original dataframe, is reorder the pattern to [7, 7, 6], moving the entire associated rows as I do this. In other words, going by the index, I want to get output that looks like this:
df.reindex([0, 1, 3, 4, 2, 5, 6])
Values Vals
0 1 6
1 3 7
3 7 9
4 7 5
2 6 7
5 8 3
6 4 1
Then, finally, I want to reset the index so that the values in all the columns stay in the new re-ordered place;
Values Vals
0 1 6
1 3 7
2 7 9
3 7 5
4 6 7
5 8 3
6 4 1
In order to use pat_i as a basis for re-ordering, I've tried to modify the second solution given here:
Python Pandas: How to move one row to the first row of a Dataframe?
target_row = 2
# Move target row to first element of list.
idx = [target_row] + [i for i in range(len(df)) if i != target_row]
However, I can't figure out how to exploit the pat_i RangeIndex object to use it with this code. The solution, when I find it, will be applied to hundreds of dataframes, each one of which will contain the [6, 7, 7] pattern that needs to be re-ordered in one place, but not the same place in each dataframe.
Any help appreciated...and I'm sure there must be an elegant, pythonic way of doing this, as it seems like it should be a common enough challenge. Thank you.
I just sort of rewrote your code. I held the first and last indexes to the side, reordered the indexes of interest, and put everything together in a new index. Then I just use the new index to reorder the data.
import pandas as pd
from pandas import RangeIndex
df = pd.DataFrame(list(zip([1, 3, 6, 7, 7, 8, 4], [6, 7, 7, 9, 5, 3, 1])), columns = ['Values', 'Vals'])
pattern = [6, 7, 7]
new_order = [1, 2, 0] # new order of pattern
for i in list(df[df['Values'] == pattern[0]].index):
if all(df['Values'][i:i+len(pattern)] == pattern):
pat_i = df[i:i+len(pattern)]
front_ind = list(range(0, pat_i.index[0]))
back_ind = list(range(pat_i.index[-1]+1, len(df)))
pat_ind = [pat_i.index[i] for i in new_order]
new_ind = front_ind + pat_ind + back_ind
df = df.loc[new_ind].reset_index(drop=True)
df
Out[82]:
Values Vals
0 1 6
1 3 7
2 7 9
3 7 5
4 6 7
5 8 3
6 4 1
I have a pandas dataframe.
One of its columns contains a list of 60 elements, constant across its rows.
How do I convert each of these lists into a row of a new dataframe?
Just to be clearer: say A is the original dataframe with n rows. One of its columns contains a list of 60 elements.
I need to create a new dataframe nx60.
My tentative:
def expand(x):
return(pd.DataFrame(np.array(x)).reshape(-1,len(x)))
df["col"].apply(lambda x: expand(x))]
it gives funny results....
The weird thing is that if i call the function "expand" on a single raw, it does exactly what I expect from it
expand(df["col"][0])
To ChootsMagoots: Thjis is the result when i try to apply your suggestion. It does not work.
Sample data
df = pd.DataFrame()
df['col'] = np.arange(4*5).reshape(4,5).tolist()
df
Output:
col
0 [0, 1, 2, 3, 4]
1 [5, 6, 7, 8, 9]
2 [10, 11, 12, 13, 14]
3 [15, 16, 17, 18, 19]
now exctract DataFrame from col
df.col.apply(pd.Series)
Output:
0 1 2 3 4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
Try this:
new_df = pd.DataFrame(df["col"].tolist())
This is a little frankensteinish, but you could also try:
import numpy as np
np.savetxt('outfile.csv', np.array(df['col'].tolist()), delimiter=',')
new_df = pd.read_csv('outfile.csv')
You can try this as well:
newCol = pd.Series(yourList)
df['colD'] = newCol.values
The above code:
1. Creates a pandas series.
2. Maps the series value to columns in original dataframe.
I am having a tough time with this one - not sure why...maybe it's the late hour.
I have a dataframe in pandas as follows:
1 10
2 11
3 20
4 5
5 10
I would like to calculate for each row the multiplicand for each row above it. For example, at row 3, I would like to calculate 10*11*20, or 2,200.
How do I do this?
Use cumprod.
Example:
df = pd.DataFrame({'A': [10, 11, 20, 5, 10]}, index=range(1, 6))
df['cprod'] = df['A'].cumprod()
Note, since your example is just a single column, a cumulative product can be done succinctly with a Series:
import pandas as pd
s = pd.Series([10, 11, 20, 5, 10])
s
# Output
0 10
1 11
2 20
3 5
4 10
dtype: int64
s.cumprod()
# Output
0 10
1 110
2 2200
3 11000
4 110000
dtype: int64
Kudos to #bananafish for locating the inherent cumprod method.