I want to add some columns with an funktion without typing each name for the colum
like this
for i in range(0,11):
df["row" + i] = [random.randrange(1, 50, 1) for i in range(7)] # after the = is an exampel
thanks for you´re help
Related
I have a csv file which has around 58 million cells containing numerical data. I want to extract data from every 16 cells which are 49 rows apart.
Let me describe it clearly.
The data I need to extract
The above image shows the the first set of data that is to be extracted (rows 23 to 26, columns 92 to 95). This data has to be written in another file csv file (preferably in a row).
Then I will move down 49 rows (row 72), then extract 4rows x 4columns. Shown in image below.
Next set of data
Similarly, I need to keep going till I reach the end of the file.
Third set
The next set will be the image shown above.
I have to keep going till I reach the end of the file and extract thousands of such data.
I had written a code for this but its not working. I don't know where is the mistake. I will also attach it here.
import pandas as pd
import numpy
df = pd.read_csv('TS_trace31.csv')
# print(numpy.shape(df))
df = pd.read_csv('TS_trace31.csv')
# print(numpy.shape(df))
arrY = []
ex = 0
for i in range(len(df)):
if i == 0:
for j in range(4):
l = (df.iloc[j+21+i*(49), 91:95]).tolist()
arrY.append(l)
else:
for j in range(4):
if j+22+i*(49) >= len(df):
ex = 1
break
# print(j)
l = (df.iloc[j+21+i*(49), 91:95]).tolist()
arrY.append(l)
if ex == 1:
break
# print(arrY)
a = []
for i in range(len(arrY) - 3):
p = arrY[i]+arrY[i+1]+arrY[i+2]+arrY[i+3]
a.append(p)
print(numpy.shape(a))
numpy.savetxt('myfile.csv', a, delimiter=',')
Using the above code, I didn't get the result I wanted.
Please help with this and correct where I have gone wrong.
I couldn't attach my csv file here, Please try to use any sample sheet that you have or can create a simple one.
Thanks in advance! Have a great day.
i don't know what exactly you are doing in your code
but i wrote my own
import csv
from itertools import chain
CSV_PATH = 'TS_trace31.csv'
new_data = []
with open(CSV_PATH, 'r') as csvfile:
reader = csv.reader(csvfile)
# row_num for storing big jumps e.g. 23, 72, 121 ...
row_num = 23
# n for storing the group number 0 - 3
# with n we can find the 23, 24, 25, 26
n = 0
# row_group for storing every 4 group rows
row_group = []
# looping over every row in main file
for row in reader:
if reader.line_num == row_num + n:
# for the first time this is going to be 23 + 0
# then we add one number to the n
# so the next cycle will be 24 and so on
n += 1
print(reader.line_num)
# add each row to it group
row_group.append(row[91:95])
# check if we are at the end of the group e.g. 26
if n == 4:
# reset the group number
n = 0
# add the jump to main row number
row_num += 49
# combine all the row_group to a single row
new_data.append(list(chain(*row_group)))
# clear the row_group for next set of rows
row_group.clear()
print('='*50)
else:
continue
# and finally write all the rows in a new file
with open('myfile.csv', 'w') as new_csvfile:
writer = csv.writer(new_csvfile)
writer.writerows(new_data)
I have a dataframe say df_dt_proc with 35 columns.
I want to add a column to the dataframe df_dt_proc['procedures'] which should have all the columns concatenated except column at index 0 separated by , .
I am able to achieve the result by the following script:
df_dt_proc['procedures'] = np.nan
_len = len(df_dt_proc.columns[1:-1])
for i in range(len(df_dt_proc)):
res = ''
for j in range(_len):
try:
res += df_dt_proc[j][i] + ', '
except:
break
df_dt_proc['procedures'][i] = res
However, there must be a more pythonic way to achieve this.
Use custom lambda function with remove NaN and Nones and converting to strings, for select all columns without first and last use DataFrame.iloc:
f = lambda x: ', '.join(x.dropna().astype(str))
df_dt_proc['procedures'] = df_dt_proc.iloc[:, 1:-1].agg(f, axis=1)
Try this with agg:
df_dt_proc['procedures'] = df_dt_proc[df_dt_proc.columns[1:-1]].astype(str).agg(', '.join, axis=1)
For a project I am working on, I am loading some csv datafile into a dataframe using read_csv. I then print the dataframe on a tkinter frame using some Entry widgets. The user can populate or edit some entries. I would like to create at the beginning of each row a checkbox so that once the user is happy with the edits I upload the "dataframe" for which checkboxes are checked into a database.
When loading the content of the dataframe I can create the desired checkboxes but unfortunately the number of rows of the input datafile is not fixed. For each checkbox I would like to have a unique variable and hence I would like to create an undetermined number of IntVar variables.
within my Tk-inherited class I have the fileLoad function
def fileLoad(self):
df = pd.read_csv(self.filename)
rows, cols = df.shape
for r in range(rows):
for c in range(cols):
e = Entry(self.ViewFrame)
e.insert(0, df.iloc[r, c])
e.grid(row=r, column=c + 1)
so how can I create a variable number of checkboxes positioned in column=0 please?
Ok I find a way as follow:
def fileLoad(self):
df = pd.read_csv(self.filename)
rows, cols = df.shape
vars = []
for r in range(rows):
var = IntVar()
vars.append(var)
ckbox = ttk.Checkbutton(self.ViewFrame, variable=var)
ckbox.grid(row=r+10, column=0)
for c in range(cols):
e = Entry(self.ViewFrame)
e.insert(0, df.iloc[r, c])
e.grid(row=r + 10, column=c + 1)
I store the variables var into a list called vars.
I am having a dataframe with 6 columns, what i need from tkinter is to print data in next column everytime when first loop iterate. For eg:
xls = pd.read_excel(file)
df = pd.DataFrame(xls)
df_col = df.columns.values
tree["columns"]=(df_col)
counter = len(df)
for x in range(len(df_col)):
tree.column(x, width=100 )
tree.heading(x, text=df_col[x])
for i in range(counter):
tree.insert("" , 0, values=(df[df_col[x]][i]))
Desired Output:
What im getting from my above code is:
Any help would be appreciated. Thanks in advance.
I have a pandas.dataframe, and I want to select certain data by some rules.
The following codes generate the dataframe
import datetime
import pandas as pd
import numpy as np
today = datetime.date.today()
dates = list()
for k in range(10):
a_day = today - datetime.timedelta(days=k)
dates.append(np.datetime64(a_day))
np.random.seed(5)
df = pd.DataFrame(np.random.randint(100, size=(10, 3)),
columns=('other1', 'actual', 'other2'),
index=['{}'.format(i) for i in range(10)])
df.insert(0, 'dates', dates)
df['err_m'] = np.random.rand(10, 1)*0.1
df['std'] = np.random.rand(10, 1)*0.05
df['gain'] = np.random.rand(10, 1)
Now, I want select by the following rules:
1. compute the sum of 'err_m' and 'std', then sort the df so that the sum is descending
2. from the result of step 1, select the part where 'actual' is > 50
Thanks
Create a new column and then sort by this one:
df['errsum'] = df['err_m'] + df['std']
# Return a sorted dataframe
df_sorted = df.sort('errsum', ascending = False)
Select the lines you want
# Create an array with True where the condition is met
selector = df_sorted['errsum'] > 50
# Return a view of sorted_dataframe with only the lines you want
df_sorted[selector]