import pandas as pd
data = {'x':['011','012','013'],'y':['022','033','041']}
Df = pd.DataFrame(data = data,type = str)
Df.to_csv("path/to/save.csv")
There result I've obtained seems as this
To achieve such result it will be easier to export directly to xlsx file, even without setting dtype of DataFrame.
import pandas as pd
writer = pd.ExcelWriter('path/to/save.xlsx')
data = {'x':['011','012','013'],'y':['022','033','041']}
Df = pd.DataFrame(data = data)
Df.to_excel(writer,"Sheet1")
writer.save()
I've tried also some other methods like prepending apostrophe or quoting all fields with ", but it gave no effect.
Related
I am trying to read data from the following link to a data frame without saving locally (this is important). I figured out a way (below), but is there an efficient way to do this?
from urllib.request import urlopen
import pandas as pd
from io import StringIO
from matplotlib.dates import DateFormatter
from datetime import datetime
uri = 'https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=AXA&data=all&year1=2022&month1=12&day1=1&year2=2022&month2=12&day2=1&tz=Etc%2FUTC&format=onlycomma&latlon=no&elev=no&missing=M&trace=T&direct=no&report_type=3&report_type=4'
data = urlopen(uri, timeout=300).read().decode("utf-8")
dateparse = lambda x: datetime.strptime(x.strip(), '%Y-%m-%d %H:%M')
str1 = data.split('\n')
dfList = []
for ii in range(1,len(str1)):
if len(str1[ii])>0:
df1 = pd.read_csv(StringIO(str1[ii]), parse_dates=[1], date_parser=dateparse, header=None) #Read each string into a dataframe
if not df1.empty:
df2 = df1.iloc[:,0:3] #Get the first five columns
if df2.iloc[0,-1] != 'M': #Don't append the ones with missing data
dfList.append(df2)
df = pd.concat(dfList, axis=0, ignore_index=True)
df.columns = ['Station','Date','Temp']
ax1 = df.plot(x=1,y=2)
ax1.get_figure().autofmt_xdate()
Using requests, pandas and io:
from io import StringIO
import pandas as pd
import requests
url = (
"https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?"
"station=AXA&data=all&year1=2022&month1=12&day1=1&year2=2022&"
"month2=12&day2=1&tz=Etc%2FUTC&format=onlycomma&latlon=no&"
"elev=no&missing=M&trace=T&direct=no&report_type=3&report_type=4"
)
with requests.Session() as request:
response = request.get(url, timeout=30)
if response.status_code != 200:
print(response.raise_for_status())
df = pd.read_csv(StringIO(response.text), sep=",")
print(df)
Iam using rpy2 to get comorbidity Index of patients , i got the results but iam not able to convert those output to pandas Dataframe
below is the code
#creating Datframe
data = {"person_id":[1,1,1,2,2,3],
"dx_1":["F11","E40","","F32","C77","G10"],
"dx_2":["F1P","E400","","F322","C737",""]}
#converting Pandas Dataframe to R Datframe using rpy2
import rpy2
from rpy2.robjects import pandas2ri
import rpy2.robjects.numpy2ri
from rpy2.robjects.packages import importr
r_dataframe = pandas2ri.py2ri(df1)
print(r_dataframe)
#installing 'comorbidity ' package using rpy2
R = rpy2.robjects.r
DTW = importr('comorbidity')
#executing comorbidity function by using one column icd_1
output = DTW.comorbidity(x = r_dataframe, id = "person_id", code = "icd_1",
score = "charlson", assign0 = False,
icd = "icd10")
print(output)
but not able to convert output to pandas dataframe
import rpy2, rpy2.robjects as robjects, rpy2.robjects.packages as rpackages
from rpy2.robjects.vectors import StrVector
#Converting data frames back and forth between rpy2 and pandas
from rpy2.robjects import r, pandas2ri
#convert output to pandas dataframe
pandas2ri.ri2py_dataframe(output)
getting below error
TypeError: Parameter 'categories' must be list-like, was
please help
Thanks in advance
I have a date string in microsecond resolution. I need it as an integer.
import pandas as pd
data = ["20181231235959383171", "20181231235959383172"]
df = pd.DataFrame(data=data, columns=["A"])
df["A"].astype(np.int)
Error:
File "pandas\_libs\lib.pyx", line 545, in pandas._libs.lib.astype_intsafe
OverflowError: Python int too large to convert to C long
Same problem if I try to cast it to standard Python int
Per my answer in your previous question:
import pandas as pd
data = ["20181231235959383171", "20181231235959383172"]
df = pd.DataFrame(data=data, columns=["A"])
# slow but big enough
df["A_as_python_int"] = df["A"].apply(int)
# fast but has to be split to two integers
df["A_seconds"] = (df["A_as_python_int"] // 1000000).astype(np.int)
df["A_fractions"] = (df["A_as_python_int"] % 1000000).astype(np.int)
You could do this:
import pandas as pd
data = ["20181231235959383171", "20181231235959383172"]
df = pd.DataFrame(data=data, columns=["A"])
before = df.A[0]
df.A = [int(x) for x in df.A.tolist()]
after = df.A[0]
before, after
Output:
The data has been cast into an integer. Showing: (before, after)
('20181231235959383171', 20181231235959383171)
I made it to the point where all tr data data has been scraped and I am able to get a nice printout. But when I go to implement the pd.DataFrame as in df= pd.DataFrame({"A": a}) etc, I get a syntax error
Here is a list of my imported libraries in the Jupyter Notebook:
import pandas as pd
import numpy as np
import bs4 as bs
import requests
import urllib.request
import csv
import html5lib
from pandas.io.html import read_html
import re
Here is my code:
source = urllib.request.urlopen('https://www.zipcodestogo.com/Texas/').read()
soup = bs.BeautifulSoup(source,'html.parser')
table_rows = soup.find_all('tr')
table_rows
for tr in table_rows:
td = tr.find_all('td')
row = [i.text for i in td]
print(row)
texas_info = pd.DataFrame({
"title": Texas
"Zip Code" : [Zip Code],
"City" :[City],
})
texas_info.head()
I expect to get a dataframe with two columns, one being the 'Zip Code' and the other the 'Cities'
If you want to create manually, with bs4 4.7.1 you can use :not, :contains and :nth-of-type pseudo classes to isolate the two columns of interest, then construct a dict then convert to df
import pandas as pd
import urllib
from bs4 import BeautifulSoup as bs
source = urllib.request.urlopen('https://www.zipcodestogo.com/Texas/').read()
soup = bs(source,'lxml')
zips = [item.text for item in soup.select('.inner_table:contains(Texas) td:nth-of-type(1):not([colspan])')]
cities = [item.text for item in soup.select('.inner_table:contains(Texas) td:nth-of-type(2):not([colspan])')]
d = {'Zips': zips,'Cities': cities}
df = pd.DataFrame(d)
df = df[1:].reset_index(drop = True)
You could combine selectors into one line:
import pandas as pd
import urllib
from bs4 import BeautifulSoup as bs
source = urllib.request.urlopen('https://www.zipcodestogo.com/Texas/').read()
soup = bs(source,'lxml')
items = [item.text for item in soup.select('.inner_table:contains(Texas) td:nth-of-type(1):not([colspan]), .inner_table:contains(Texas) td:nth-of-type(2):not([colspan])')]
d = {'Zips': items[0::2],'Cities': items[1::2]}
df = pd.DataFrame(d)
df = df[1:].reset_index(drop = True)
print(df)
I note you want to create manually but worth knowing for future readers that you could just use pandas read_html
import pandas as pd
table = pd.read_html('https://www.zipcodestogo.com/Texas/')[1]
table.columns = table.iloc[1]
table = table[2:]
table = table.drop(['Zip Code Map', 'County'], axis=1).reset_index(drop=True)
print(table)
Try creating the DataFrame and perform the for loop to append each row in the table into the DataFrame.
df = pd.DataFrame()
for tr in table_rows:
td = tr.find_all('td')
row = [i.text for i in td]
print(row)
zipCode = row[0] # assuming first column
city = row[1] # assuming second column
df = df.append({"Zip Code": zipCode, "City" : city}, ignore_index=True)
If you only need these two columns, you should not include title in the DataFrame (that will create another column); that line also happened to be where the syntax error occurred because of the missing comma.
I am fetching excel data from django database via raw query. excel is generated but column header is missing .
please suggest some way to get that header.
import pandas as pd
from pandas import ExcelWriter
df1 = pd.DataFrame(row1)
try:
from StringIO import StringIO
except:
from io import StringIO
import xlwt
wb = Workbook()
writer = ExcelWriter("XYZ.xlsx",options={'remove_timezone': True})
xl_out = StringIO()
writer.path = xl_out
ws1 = wb.add_sheet("abc")
for col_num, value in enumerate(df1.columns.values):
ws1.write(1,col_num + 1, 'value')
df1.to_excel(writer,"abc", index= True, header=True)
writer.save()