Have data in dataframe and need to compare current value of one column and prior of value of another column. Current time is row 5 in this dataframe and here's the desired output:
target data is streamed and captured into a DataFrame, then that array is multiplied by a constant to generate another column, however unable to generate the third column comp, which should compare current value of prod with prior value of the comp from comp.
df['temp'] = self.temp
df['prod'] = df['temp'].multiply(other=const1)
Another user had suggested using this logic but it is generates errors because the routine's array doesn't match the size of the DataFrame:
for i in range(2, len(df['temp'])):
df['comp'].append(max(df['prod'][i], df['comp'][i - 1]))
Let's try this, I think this will capture your intended logic:
df = pd.DataFrame({'col0':[1,2,3,4,5]
,'col1':[5,4.9,5.5,3.5,6.3]
,'col2':[2.5,2.45,2.75,1.75,3.15]
})
df['col3'] = df['col2'].shift(-1).cummax().shift()
print(df)
Output:
col0 col1 col2 col3
0 1 5.0 2.50 NaN
1 2 4.9 2.45 2.45
2 3 5.5 2.75 2.75
3 4 3.5 1.75 2.75
4 5 6.3 3.15 3.15
Related
I am new to Python and lost in the way to approach this problem: I have a dataframe where the information I need is mostly grouped in layers of 2,3 and 4 rows. Each group has a different ID in one of the columns. I need to create another dataframe where the groups of rows are now a single row, where the information is unstacked in more columns. Later I can drop unwanted/redundant columns.
I think I need to iterate through the dataframe rows and filter for each ID unstacking the rows into a new dataframe. I cannot obtain much from unstack or groupby functions. Is there a easy function or combination that can make this task?
Here is a sample of the dataframe:
2_SH1_G8_D_total;Positions tolerance d [z] ;"";0.000; ;0.060;"";0.032;0.032;53%
12_SH1_G8_D_total;Positions tolerance d [z] ;"";-58.000;"";"";"";---;"";""
12_SH1_G8_D_total;Positions tolerance d [z] ;"";-1324.500;"";"";"";---;"";""
12_SH1_G8_D_total;Positions tolerance d [z] ;"";391.000;"";"";"";390.990;"";""
13_SH1_G8_D_total;Flatness;"";0.000; ;0.020;"";0.004;0.004;20%
14_SH1_G8_D_total;Parallelism tolerance ;"";0.000; ;0.030;"";0.025;0.025;84%
15_SH1_B1_B;Positions tolerance d [x y] ;"";0.000; ;0.200;"";0.022;0.022;11%
15_SH1_B1_B;Positions tolerance d [x y] ;"";265.000;"";"";"";264.993;"";""
15_SH1_B1_B;Positions tolerance d [x y] ;"";1502.800;"";"";"";1502.792;"";""
15_SH1_B1_B;Positions tolerance d [x y] ;"";-391.000;"";"";"";---;"";""
The original dataframe has information in 4 rows, but not always. Ending dataframe should have only one row per Id occurrence, with all the info in the columns.
So far, with help, I managed to run this code:
with open(path, newline='') as datafile:
data = csv.reader(datafile, delimiter=';')
for row in data:
tmp.append(row)
# Create data table joining data with the same GAT value, GAT is the ID I need
Data = []
Data.append(tmp[0])
GAT = tmp[0][0]
j = 0
counter = 0
for i in range(0,len(tmp)):
if tmp[i][0] == GAT:
counter = counter + 1
if counter == 2:
temp=(tmp[i][5],tmp[i][7],tmp[i][8],tmp[i][9])
else:
temp = (tmp[i][3], tmp[i][7])
Data[j].extend(temp)
else:
Data.append(tmp[i])
GAT = tmp[i][0]
j = j + 1
# for i in range(0,len(Data)):
# print(Data[i])
with open('output.csv', 'w', newline='') as outputfile:
writedata = csv.writer(outputfile, delimiter=';')
for i in range(0, len(Data)):
writedata.writerow(Data[i]);
But is not really using pandas, which probably will give me more power handling the data. In addition, this open() commands have troubles with the non-ascii characters I am unable to solve.
Is there a more elegant way using pandas?
So basically you're doing a "partial transpose". Is this what you want (referenced from this answer)?
Sample Data
With unequal number of rows per line
ID col1 col2
0 A 1.0 2.0
1 A 3.0 4.0
2 B 5.0 NaN
3 B 7.0 8.0
4 B 9.0 10.0
5 B NaN 12.0
Code
import pandas as pd
import io
# read df
df = pd.read_csv(io.StringIO("""
ID col1 col2
A 1 2
A 3 4
B 5 nan
B 7 8
B 9 10
B nan 12
"""), sep=r"\s{2,}", engine="python")
# solution
g = df.groupby('ID').cumcount()
df = df.set_index(['ID', g]).unstack().sort_index(level=1, axis=1)
df.columns = [f'{a}_{b+1}' for a, b in df.columns]
Result
print(df)
col1_1 col2_1 col1_2 col2_2 col1_3 col2_3 col1_4 col2_4
ID
A 1.0 2.0 3.0 4.0 NaN NaN NaN NaN
B 5.0 NaN 7.0 8.0 9.0 10.0 NaN 12.0
Explanation
After the .set_index(["ID", g]) step, the dataset becomes
col1 col2
ID
A 0 1.0 2.0
1 3.0 4.0
B 0 5.0 NaN
1 7.0 8.0
2 9.0 10.0
3 NaN 12.0
where the multi-index is perfect for df.unstack().
I am trying to create a new DF by subtracting the subsequent rows of a DF. There is no column that I can use to group by and subtract. the operation should subtract row 0 with row 1, row 2 with row 3, row 4 with row 5, and so on.
df1:
needs to result in df2:
I tried to iterate over rows using
for ii in [0,2,4]:
and using the value in ii for .iloc, but the result is a column df instead of a row.
You can calculate the .diff of each row from the previous, then select the even elements from that via iloc:
out = df.diff(-1).iloc[::2].reset_index(drop=True)
print(out)
a b c d
0 28.0 1.0 147.0 -7.0
1 87.0 10.0 151.0 -7.0
2 -2.0 1.0 77.0 -7.0
Not that the subtraction in your sample output isn't correct (unless I mistunderstood something, which is entirely possible). But for your first row columns "a", performing 633 - 605 should be 28, not 27. Then in column "d": 1 - 8 is -7, not -8
Here is a code sniplet to simulate the problem i am facing. i am using iteration on large datasets
df = pd.DataFrame({'grp':np.random.choice([1,2,3,4,5],500),'col1':np.arange(0,500),'col2':np.random.randint(0,10,500),'col3':np.nan})
for index, row in df.iterrows():
#based on group label, get last 3 values to calculate mean
d=df.iloc[0:index].groupby('grp')
try:
dgrp_sum=d.get_group(row.grp).col2.tail(3).mean()
except:
dgrp_sum=999
#after getting last 3 values of group with reference to current row reference, multiply by other rows
df.at[index,'col3']=dgrp_sum*row.col1*row.col2
if i want to speed it up with vectors, how do i convert this code?
You basically calculate moving average over every group.
Which means you can group dataframe by "grp" and calculate rolling mean.
At the end you multiply columns in each row because it is not dependent on group.
df["col3"] = df.groupby("grp").col2.rolling(3, min_periods=1).mean().reset_index(0,drop=True)
df["col3"] = df[["col1", "col2", "col3"]].product(axis=1)
Note: In your code, each calculated mean is placed in the next row, thats why you probably have this try block.
# Skipping last product gives only mean
# np.random.seed(1234)
# print(df[df["grp"] == 2])
grp col1 col2 iter mask
4 2 4 6 999.000000 6.000000
5 2 5 0 6.000000 3.000000
6 2 6 9 3.000000 5.000000
17 2 17 1 5.000000 3.333333
27 2 27 9 3.333333 6.333333
I am working with dataframes in the pandas library. I have a table of data in Excel that I save as a CSV then I call
df = pd.read_csv("file.csv")
I expect the frame to look something like
Item1 Item2 Item3
0 12.00 3 2
1 4.00 8 4
2 3.14 2 8
But instead I get
Item1 Item2 Item3 Unnamed: 3 Unnamed: 4
0 12.00 3 2 NaN NaN
1 4.00 8 4 NaN NaN
2 3.14 2 8 NaN NaN
Or sometimes extra rows with all NaN values. It appears that pandas is not aware of the real size of the CSV. The data in Excel is organized perfectly fine, the data values are all nonempty and are entirely in a rectangle. How do I fix this? Is there an edit I can make to the CSV that will specify the its correct size?
As requested here is a snippet of the data. It goes down to about 2500 rows, and there are no more values to the right.
You probably have a cell that is not empty (for example a space ) in the original Excel file. If you are getting 2 unnamed columns in pandas, try to delete 2 columns of the original Excel file.
Another way would be to keep all columns that are not unnamed. You could do this with:
real_cols = [x for x in df.columns if not x.startswith("Unnamed: ")]
df = df[real_cols]
And then you can save the csv.
I am reading a large file that contains ~9.5 million rows x 16 cols.
I am interested in retrieving a representative sample, and since the data is organized by time, I want to do this by selecting every 500th element.
I am able to load the data, and then select every 500th row.
My question: Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
Question 2: How would you approach this problem if the date column was not ordered? At the moment, I am assuming it's ordered by date, but all data is prone to errors.
Here is a snippet of what the data looks like (first five rows) The first 4 rows are out of order, bu the remaining dataset looks ordered (by time):
VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID payment_type fare_amount extra mta_tax tip_amount tolls_amount improvement_surcharge total_amount
0 1 2017-01-09 11:13:28 2017-01-09 11:25:45 1 3.30 1 N 263 161 1 12.5 0.0 0.5 2.00 0.00 0.3 15.30
1 1 2017-01-09 11:32:27 2017-01-09 11:36:01 1 0.90 1 N 186 234 1 5.0 0.0 0.5 1.45 0.00 0.3 7.25
2 1 2017-01-09 11:38:20 2017-01-09 11:42:05 1 1.10 1 N 164 161 1 5.5 0.0 0.5 1.00 0.00 0.3 7.30
3 1 2017-01-09 11:52:13 2017-01-09 11:57:36 1 1.10 1 N 236 75 1 6.0 0.0 0.5 1.70 0.00 0.3 8.50
4 2 2017-01-01 00:00:00 2017-01-01 00:00:00 1 0.02 2 N 249 234 2 52.0 0.0 0.5 0.00 0.00 0.3 52.80
Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
Something you could do is to use the skiprows parameter in read_csv, which accepts a list-like argument to discard the rows of interest (and thus, also select). So you could create a np.arange with a length equal to the amount of rows to read, and remove every 500th element from it using np.delete, so this way we'll only be reading every 500th row:
n_rows = 9.5e6
skip = np.arange(n_rows)
skip = np.delete(skip, np.arange(0, n_rows, 500))
df = pd.read_csv('my_file.csv', skiprows = skip)
Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
First get the length of the file by a custom function, remove each 500 row by numpy.setdiff1d and pass to the skiprows parameter in read_csv:
#https://stackoverflow.com/q/845058
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
len_of_file = file_len('test.csv')
print (len_of_file)
skipped = np.setdiff1d(np.arange(len_of_file), np.arange(0,len_of_file,500))
print (skipped)
df = pd.read_csv('test.csv', skiprows=skipped)
How would you approach this problem if the date column was not ordered? At the moment, I am assuming it's ordered by date, but all data is prone to errors.
The idea is read only the datetime column by parameter usecols, and then sort and select each 500 index value, get the difference and pass again to parameter skiprows:
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
len_of_file = file_len('test.csv')
df1 = pd.read_csv('test.csv',
usecols=['tpep_pickup_datetime'],
parse_dates=['tpep_pickup_datetime'])
sorted_idx = (df1['tpep_pickup_datetime'].sort_values()
.iloc[np.arange(0,len_of_file,500)].index)
skipped = np.setdiff1d(np.arange(len_of_file), sorted_idx)
print (skipped)
df = pd.read_csv('test.csv', skiprows=skipped).sort_values(by=['tpep_pickup_datetime'])
use a lambda with skiprows:
pd.read_csv(path, skiprows=lambda i: i % N)
to skip every N rows.
source: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
You can use csv module return a iterator and use itertools.cycle to select every nth row.
import csv
from itertools import cycle
source_file='D:/a.txt'
cycle_size=500
chooser = (x == 0 for x in cycle(range(cycle_size)))
with open(source_file) as f1:
rdr = csv.reader(f1)
data = [row for pick, row in zip(chooser, rdr) if pick]