I'm using this tweets dataset with Pyspark in order to process it and get some trends according to the tweet's location. But I'm having a problem when I try to create the dataframe. I'm using spark.read.options(header="True").csv("hashtag_donaldtrump.csv") to create the dataframe, but if I look at the tweets column, this is the result I get:
Do you know how can I clean the CSV file so it can be processed by Spark? Thank you in advance!
It looks like a multiline csv. Try doing
df = spark.read.csv("hashtag_donaldtrump.csv", header=True, multiLine=True)
Related
I need to clean up a files using Pandas. But the raw files we are using have a couple of rows above the column headers that I need to erase before getting to work. I do not find how to get rid of them.
I suppose this has to be done before generating the frame.
Can someone help?
Thanks in advance.
Sample CSV raw file
You can try using the skiprows parameter in read_csv() :
pd.read_csv('filename.csv', skiprows=5)
Using Pandas, I'm trying to read an excel file that looks like the following:
another sample
I tried to read the excel file using the regular approach by running: df = pd.read_excel('filename.xlsx', skiprows=6).
But the problem with it is that I don't get all the columns names needed and most of the column names are Unnamed:1
Is there a way to solve these and read all the columns? Or an approach were I can convert it to a json file
I have a messy text file that I need to sort into columns in a dataframe so I
can do the data analysis I need to do. Here is the messy looking file:
Messy text
I can read it in as a csv file, that looks a bit nicer using:
import pandas as pd
data = pd.read_csv('phx_30kV_indepth_0_0_outfile.txt')
print(data)
And this prints out the data aligned, but the issue is that the output is [640 rows x 1 column]. And I need to separate it into multiple columns and manipulate it as a dataframe.
I have tried a number of solutions using StringIO that have worked here before, but nothing seems to be doing the trick.
However, when I do this, there is the issue that the
delim_whitespace=True
Link to docs ^
df = pd.read_csv('phx_30kV_indepth_0_0_outfile.txt', delim_whitespace=True)
Your input file is actually not in CSV format.
As you provided only .png picture, it is even not clear, whether this file
is divided into rows or not.
If not, you have to start from "cutting" the content into individual lines and
read the content from the output file - result of this cutting.
I think, this is the first step, before you can use either read_csv or read_table (of course, with delim_whitespace=True).
I have a large file which is dynamically generated, a small sample of which is given below:
ID,FEES,I_CLSS
11,5555,00000110
12,5555,654321
13,5555,000030
14,5555,07640
15,5555,14550
17,5555,99070
19,5555,090090
My issue is that I will always have a column like I_CLSS that is starting with 0s in this file. I'd like to read the file to a spark dataframe with I_CLSS column as StringType.
For this, in python I can do something like,
df = pandas.read_csv('INPUT2.csv',dtype={'I_CLSS': str})
But is there an alternative to this command in pyspark?
I understand that I can manually specify the schema of a file in Pyspark. But it would be extremely diffficult to do for a file whose columns are dynamically generated.
So I'd appreciate it if somebody could help me with this.
I am trying to restructure the way my precipitations' data is being organized in an excel file. To do this, I've written the following code:
import pandas as pd
df = pd.read_excel('El Jem_Souassi.xlsx', sheetname=None, header=None)
data=df["El Jem"]
T=[]
for column in range(1,56):
liste=data[column].tolist()
for row in range(1,len(liste)):
liste[row]=str(liste[row])
if liste[row]!='nan':
T.append(liste[row])
result=pd.DataFrame(T)
result
This code works fine and through Jupyter I can see that the result is good
screenshot
However, I am facing a problem when attempting to save this dataframe to a csv file.
result.to_csv("output.csv")
The resulting file contains the vertical index column and it seems I am unable to call for a specific cell.
(Hopefully, someone can help me with this problem)
Many thanks !!
It's all in the docs.
You are interested in skipping the index column, so do:
result.to_csv("output.csv", index=False)
If you also want to skip the header add:
result.to_csv("output.csv", index=False, header=False)
I don't know how your input data looks like (it is a good idea to make it available in your question). But note that currently you can obtain the same results just by doing:
import pandas as pd
df = pd.DataFrame([0]*16)
df.to_csv('results.csv', index=False, header=False)