col1, col2, geometry
11.54000000,0.00000000,"{"type":"Polygon","coordinates":[[[-61.3115751786311,-33.83968838375797],[-61.29737019968823,-33.83207774370677],[-61.29443049860791,-33.83592770721248],[-61.29241347742871,-33.83489393774538],[-61.28994584513501,-33.83806650089736],[-61.292499308117186,-33.83938539699006],[-61.28958106470898,-33.8431993873636],[-61.29307859612687,-33.84495487100211],[-61.295256567865046,-33.846135537383866],[-61.296388484054326,-33.84676149889543],[-61.296747927196776,-33.84651421268175],[-61.297498943449426,-33.84670133707654],[-61.297992472179686,-33.847120134589964],[-61.299741220055196,-33.84901812154847],[-61.3012164422457,-33.85018089588664],[-61.3015892874819,-33.850566250375365],[-61.30284190607861,-33.85079121660985],[-61.30496105223345,-33.848193766906206],[-61.306084952130036,-33.84682375029292],[-61.30707604410075,-33.845532812572294],[-61.30672627175046,-33.84527169005647],[-61.306290670206494,-33.845188781884744],[-61.304604048903514,-33.847304098561025],[-61.30309763921784,-33.84654473836309],[-61.30013213880613,-33.84478736144466],[-61.30110629620797,-33.8431690707163],[-61.303046037678854,-33.844170576767105],[-61.30433047221653,-33.84266156764314],[-61.30484242472771,-33.842899106713375],[-61.30696068650711,-33.844104878773436],[-61.306418212892446,-33.84505221083753],[-61.307163201216696,-33.845464893960255],[-61.30760172622554,-33.84490909256552],[-61.307932962646014,-33.844513681420494],[-61.309176116985405,-33.84280834206188],[-61.30596211112515,-33.841126948963954],[-61.3056475423994,-33.841449215098756],[-61.30526859890979,-33.841557611902374],[-61.30483601097522,-33.84149669494795],[-61.30448925534122,-33.84120408616046],[-61.30410688411086,-33.840609953572034],[-61.30400151682434,-33.839925243738094],[-61.30240379835875,-33.83889223688216],[-61.30188418287129,-33.838444480832685],[-61.301130848179525,-33.83943255499186],[-61.30078636095504,-33.83996223583909],[-61.30059265818967,-33.84016469670277],[-61.30048478527255,-33.840438447848506],[-61.300252198180424,-33.84026774340676],[-61.29876711207748,-33.839489883020924],[-61.29799408649143,-33.840597902688785],[-61.297669258508,-33.84103160870988],[-61.297566592962134,-33.84112444052047],[-61.29748538503245,-33.841083604060834],[-61.297140578061956,-33.84134946797752],[-61.29709617977233,-33.84160419097128],[-61.297170540239335,-33.84168254110631],[-61.297341460506956,-33.84179653572337],[-61.297243418161194,-33.84197105818567],[-61.29699517169225,-33.84200300239938],[-61.29680176950715,-33.84179064473802],[-61.29691703393983,-33.8416707218475],[-61.297053755769845,-33.841604265738546],[-61.29707920124143,-33.84154875978832],[-61.29709391784669,-33.84147543150246],[-61.29711262215961,-33.84133768608576],[-61.296951411710374,-33.84119216012805],[-61.297262269660294,-33.84089514360839],[-61.297626491077864,-33.84051497848962],[-61.29865532547658,-33.83935363544152],[-61.30027710358755,-33.84011486145675],[-61.30046658230606,-33.83996490243917],[-61.30063460268783,-33.83979712050095],[-61.300992098665965,-33.8393813535522],[-61.301799802937595,-33.83832425565103],[-61.30135527704997,-33.837671541923235],[-61.30082030025984,-33.83731962483044],[-61.299512855628244,-33.83689640801839],[-61.29879550338594,-33.8363083288346],[-61.29831419490918,-33.835559835856905],[-61.298360098160686,-33.83408067231082],[-61.29976541168753,-33.83467181800819],[-61.30104200723692,-33.83586895614681],[-61.30133434017162,-33.83606352507277],[-61.30153415160492,-33.836339043812224],[-61.30164813329583,-33.83657891551336],[-61.30124575062752,-33.83743146168004],[-61.30195917352424,-33.83831965157767],[-61.30196183786503,-33.83843401993221],[-61.30250094586367,-33.83890484694379],[-61.304002690127376,-33.83984352469762],[-61.30473149692381,-33.8397514189025],[-61.3054487998093,-33.839941491549894],[-61.30582354557356,-33.84016574092716],[-61.30604808932503,-33.84046128014441],[-61.306143888278996,-33.840801374736316],[-61.30598219492593,-33.841088001849094],[-61.30757239940571,-33.841967156609876],[-61.30920555104759,-33.84277500140921],[-61.3115751786311,-33.83968838375797],[-61.3115751786311,-33.83968838375797]]]}"
How do I read a csv with syntax like above?
I am doing:
import pandas as pd
df = pd.read_csv('file.csv')
However, read_csv gets confused with the , within "{"type":"Polygon","coordinates": I want it to ignore the , within the quotes.
Your csv file contains a MultiIndex, which is causing your read and split issues.
I have tried multiple methods to read your file correctly. The best method that I have found so far is using the Python engine with an advanced separator in the read_csv function.
import pandas as pd
# these are for viewing the output
pd.set_option('display.max_columns', 30)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 120)
# The separator matches the format of the string that you provided.
# I'm sure that it can be modified to be more efficient.
df = pd.read_csv('test.csv', skiprows=1, sep='(\d{1,2}.\d{1,8}),(\d{1,2}.\d{1,8}),("{"type":.*)',engine="python")
# some cleanup
df = df.drop(df.columns[0], axis=1)
# I had to save the processed file
df.to_csv('test_01.csv')
# read in the new file
df = pd.read_csv('test_01.csv', header=None, index_col=0)
print(df.to_string(index=False))
11.54 0.0 "{"type":"Polygon","coordinates":[[[-61.3115751786311,-33.83968838375797],[-61.29737019968823,-33.83207774370677],[-61.29443049860791,-33.83592770721248],[-61.29241347742871,-33.83489393774538],[-61.28994584513501,-33.83806650089736],[-61.292499308117186,-33.83938539699006],[-61.28958106470898,-33.8431993873636],[-61.29307859612687,-33.84495487100211],[-61.295256567865046,-33.846135537383866],[-61.296388484054326,-33.84676149889543],[-61.296747927196776,-33.84651421268175],[-61.297498943449426,-33.84670133707654],[-61.297992472179686,-33.847120134589964],[-61.299741220055196,-33.84901812154847],[-61.3012164422457,-33.85018089588664],[-61.3015892874819,-33.850566250375365],[-61.30284190607861,-33.85079121660985],[-61.30496105223345,-33.848193766906206],[-61.306084952130036,-33.84682375029292],[-61.30707604410075,-33.845532812572294],[-61.30672627175046,-33.84527169005647],[-61.306290670206494,-33.845188781884744],[-61.304604048903514,-33.847304098561025],[-61.30309763921784,-33.84654473836309],[-61.30013213880613,-33.84478736144466],[-61.30110629620797,-33.8431690707163],[-61.303046037678854,-33.844170576767105],[-61.30433047221653,-33.84266156764314],[-61.30484242472771,-33.842899106713375],[-61.30696068650711,-33.844104878773436],[-61.306418212892446,-33.84505221083753],[-61.307163201216696,-33.845464893960255],[-61.30760172622554,-33.84490909256552],[-61.307932962646014,-33.844513681420494],[-61.309176116985405,-33.84280834206188],[-61.30596211112515,-33.841126948963954],[-61.3056475423994,-33.841449215098756],[-61.30526859890979,-33.841557611902374],[-61.30483601097522,-33.84149669494795],[-61.30448925534122,-33.84120408616046],[-61.30410688411086,-33.840609953572034],[-61.30400151682434,-33.839925243738094],[-61.30240379835875,-33.83889223688216],[-61.30188418287129,-33.838444480832685],[-61.301130848179525,-33.83943255499186],[-61.30078636095504,-33.83996223583909],[-61.30059265818967,-33.84016469670277],[-61.30048478527255,-33.840438447848506],[-61.300252198180424,-33.84026774340676],[-61.29876711207748,-33.839489883020924],[-61.29799408649143,-33.840597902688785],[-61.297669258508,-33.84103160870988],[-61.297566592962134,-33.84112444052047],[-61.29748538503245,-33.841083604060834],[-61.297140578061956,-33.84134946797752],[-61.29709617977233,-33.84160419097128],[-61.297170540239335,-33.84168254110631],[-61.297341460506956,-33.84179653572337],[-61.297243418161194,-33.84197105818567],[-61.29699517169225,-33.84200300239938],[-61.29680176950715,-33.84179064473802],[-61.29691703393983,-33.8416707218475],[-61.297053755769845,-33.841604265738546],[-61.29707920124143,-33.84154875978832],[-61.29709391784669,-33.84147543150246],[-61.29711262215961,-33.84133768608576],[-61.296951411710374,-33.84119216012805],[-61.297262269660294,-33.84089514360839],[-61.297626491077864,-33.84051497848962],[-61.29865532547658,-33.83935363544152],[-61.30027710358755,-33.84011486145675],[-61.30046658230606,-33.83996490243917],[-61.30063460268783,-33.83979712050095],[-61.300992098665965,-33.8393813535522],[-61.301799802937595,-33.83832425565103],[-61.30135527704997,-33.837671541923235],[-61.30082030025984,-33.83731962483044],[-61.299512855628244,-33.83689640801839],[-61.29879550338594,-33.8363083288346],[-61.29831419490918,-33.835559835856905],[-61.298360098160686,-33.83408067231082],[-61.29976541168753,-33.83467181800819],[-61.30104200723692,-33.83586895614681],[-61.30133434017162,-33.83606352507277],[-61.30153415160492,-33.836339043812224],[-61.30164813329583,-33.83657891551336],[-61.30124575062752,-33.83743146168004],[-61.30195917352424,-33.83831965157767],[-61.30196183786503,-33.83843401993221],[-61.30250094586367,-33.83890484694379],[-61.304002690127376,-33.83984352469762],[-61.30473149692381,-33.8397514189025],[-61.3054487998093,-33.839941491549894],[-61.30582354557356,-33.84016574092716],[-61.30604808932503,-33.84046128014441],[-61.306143888278996,-33.840801374736316],[-61.30598219492593,-33.841088001849094],[-61.30757239940571,-33.841967156609876],[-61.30920555104759,-33.84277500140921],[-61.3115751786311,-33.83968838375797],[-61.3115751786311,-33.83968838375797]]]}"
Try this:
pd.read_csv('file.csv',quotechar='"',skipinitialspace=True)
Related
I am trying to read a csv file using pandas as so:
df = pd.read_csv('file.csv')
Here is the file before:
,schoolId,Name,Meetings Present
0,991,Jimmy Nuetron,2
1,992,Jimmy Fuetron,6
2,993,Cam Nuetron,4
Here is the file after:
,Unnamed: 0,schoolId,Name,Meetings Present
0,0.0,991.0,Jimmy Nuetron,2.0
1,1.0,992.0,Jimmy Fuetron,6.0
2,2.0,993.0,Cam Nuetron,4.0
0,,,,3
Why is it adding the numbers and columns when I run the read_csv method?
How can I prevent this without adding a seperator?
pandas.read_csv is actuallly not adding the column Unnamed: 0 because it already exists in your .csv (who apparently/probably was generated by the method pandas.DataFrame.to_csv).
You can get rid of this (extra) column by making it as an index :
df = pd.read_csv('file.csv', index_col=0)
I have a .csv file with 100 rows of data displayed like this
"Jim 1234"
"Sam 1235"
"Mary 1236"
"John 1237"
What I'm trying to achieve is splitting the numbers from the names into 2 columns in python
edit*
Using,
import pandas as pd
df = pd.read_csv('test.csv', sep='\s+')
df.to_csv('result.csv', index=False)
I managed to get it to display like this in excel
However, the numbers still do not show up in column B as I expected.
Your data have only one column and a tab delimiter:
pd.read_csv('test.csv', quoting=1, header=None, squeeze=True) \
.str.split('\t', expand=True) \
.to_csv('result.csv', index=False, header=False)
very simple way,
data=pd.DataFrame(['Jim1234','Sam4546'])
data[0].str.split('(\d+)', expand=True)
if your file resemble to the picture below then the next code will work csv file content
import pandas as pd
df = pd.read_csv('a.csv', header=None, delimiter='\s')
df
code execution
I would like to create a pandas dataframe out of a list variable.
With pd.DataFrame() I am not able to declare delimiter which leads to just one column per list entry.
If I use pd.read_csv() instead, I of course receive the following error
ValueError: Invalid file path or buffer object type: <class 'list'>
If there a way to use pd.read_csv() with my list and not first save the list to a csv and read the csv file in a second step?
I also tried pd.read_table() which also need a file or buffer object.
Example data (seperated by tab stops):
Col1 Col2 Col3
12 Info1 34.1
15 Info4 674.1
test = ["Col1\tCol2\tCol3", "12\tInfo1\t34.1","15\tInfo4\t674.1"]
Current workaround:
with open(f'{filepath}tmp.csv', 'w', encoding='UTF8') as f:
[f.write(line + "\n") for line in consolidated_file]
df = pd.read_csv(f'{filepath}tmp.csv', sep='\t', index_col=1 )
import pandas as pd
df = pd.DataFrame([x.split('\t') for x in test])
print(df)
and you want header as your first row then
df.columns = df.iloc[0]
df = df[1:]
It seems simpler to convert it to nested list like in other answer
import pandas as pd
test = ["Col1\tCol2\tCol3", "12\tInfo1\t34.1","15\tInfo4\t674.1"]
data = [line.split('\t') for line in test]
df = pd.DataFrame(data[1:], columns=data[0])
but you can also convert it back to single string (or get it directly form file on socket/network as single string) and then you can use io.BytesIO or io.StringIO to simulate file in memory.
import pandas as pd
import io
test = ["Col1\tCol2\tCol3", "12\tInfo1\t34.1","15\tInfo4\t674.1"]
single_string = "\n".join(test)
file_like_object = io.StringIO(single_string)
df = pd.read_csv(file_like_object, sep='\t')
or shorter
df = pd.read_csv(io.StringIO("\n".join(test)), sep='\t')
This method is popular when you get data from network (socket, web API) as single string or data.
I am writing a pyspark program that takes a txt file and then add a few columns to the left(beginning) of the columns in the file.
My text file looks like this:
ID,Name,Age
1233,James,15
After I run the program I want it to add two columns named creation_DT and created_By to the left of the table. I am trying to get it to look like this:
Creation_DT,Created_By,ID,Name,Age
"current timestamp", Sean,1233,James,15
This code below get my required output but I was wondering if there was an easier way to do this to optimize my script below using pyspark.
import pandas as pd
import numpy as np
with open
df = pd.read_csv("/home/path/Sample Text Files/sample5.txt", delimiter = ",")
df=pd.DataFrame(df)
df.insert(loc=0, column='Creation_DT', value=pd.to_datetime('today'))
df.insert(loc=1, column='Create_BY',value="Sean")
df.write("/home/path/new/new_file.txt")
Any ideas or suggestions?
yes it is relatively easy to convert to pyspark code
from pyspark.sql import DataFrame, functions as sf
import datetime
# read in using dataframe reader
# path here if you store your csv in local, should use file:///
# or use hdfs:/// if you store your csv in a cluster/HDFS.
spdf = (spark.read.format("csv").option("header","true")
.load("file:///home/path/Sample Text Files/sample5.txt"))
spdf2 = (
spdf
.withColumn("Creation_DT", sf.lit(datetime.date.today().strftime("%Y-%m-%d")))
.withColumn("Create_BY", sf.lit("Sean"))
spdf2.write.csv("file:///home/path/new/new_file.txt")
this code assumes you are appending the creation_dt or create_by using the same value.
I don't see you use any pyspark in your code, so I'll just use pandas this way:
cols = df.columns
df['Creation_DT'] =pd.to_datetime('today')
df['Create_BY']="Sean"
cols = cols.insert(0, 'Create_BY')
cols = cols.insert(0, 'Creation_DT')
df.columns = cols
df.write("/home/path/new/new_file.txt")
I am loading a txt file containig a mix of float and string data. I want to store them in an array where I can access each element. Now I am just doing
import pandas as pd
data = pd.read_csv('output_list.txt', header = None)
print data
This is the structure of the input file: 1 0 2000.0 70.2836942112 1347.28369421 /file_address.txt.
Now the data are imported as a unique column. How can I divide it, so to store different elements separately (so I can call data[i,j])? And how can I define a header?
You can use:
data = pd.read_csv('output_list.txt', sep=" ", header=None)
data.columns = ["a", "b", "c", "etc."]
Add sep=" " in your code, leaving a blank space between the quotes. So pandas can detect spaces between values and sort in columns. Data columns is for naming your columns.
I'd like to add to the above answers, you could directly use
df = pd.read_fwf('output_list.txt')
fwf stands for fixed width formatted lines.
You can do as:
import pandas as pd
df = pd.read_csv('file_location\filename.txt', delimiter = "\t")
(like, df = pd.read_csv('F:\Desktop\ds\text.txt', delimiter = "\t")
#Pietrovismara's solution is correct but I'd just like to add: rather than having a separate line to add column names, it's possible to do this from pd.read_csv.
df = pd.read_csv('output_list.txt', sep=" ", header=None, names=["a", "b", "c"])
you can use this
import pandas as pd
dataset=pd.read_csv("filepath.txt",delimiter="\t")
If you don't have an index assigned to the data and you are not sure what the spacing is, you can use to let pandas assign an index and look for multiple spaces.
df = pd.read_csv('filename.txt', delimiter= '\s+', index_col=False)
Based on the latest changes in pandas, you can use, read_csv , read_table is deprecated:
import pandas as pd
pd.read_csv("file.txt", sep = "\t")
If you want to load the txt file with specified column name, you can use the code below. It worked for me.
import pandas as pd
data = pd.read_csv('file_name.txt', sep = "\t", names = ['column1_name','column2_name', 'column3_name'])
You can import the text file using the read_table command as so:
import pandas as pd
df=pd.read_table('output_list.txt',header=None)
Preprocessing will need to be done after loading
I usually take a look at the data first or just try to import it and do data.head(), if you see that the columns are separated with \t then you should specify sep="\t" otherwise, sep = " ".
import pandas as pd
data = pd.read_csv('data.txt', sep=" ", header=None)
You can use it which is most helpful.
df = pd.read_csv(('data.txt'), sep="\t", skiprows=[0,1], names=['FromNode','ToNode'])