I need help about read csv file with pandas.
I have a .csv file that recorded machine parameters and want to read this excel with pandas and analyze. But problem is this excel file not in a proper table format. That means there are a lot of empty rows and columns. Also parameter values are starting from 301st line (example).
How can I read as properly this csv file?
You can use skiprows:
pd.read_csv(csv_file, skiprows=301)
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
I have some data in the .csv format file given in the link below.
https://drive.google.com/file/d/1kBtK-uBhZEyCMQ2ndHpQ1Rqd6LZ3sVJJ/view?usp=sharing
I have converted it into a pandas dataframe. My question is how do I convert it into bi-grams as in a pandas dataframe fo bigrams?
(Usually we use [i:i+n] for text but here I am dealing with columns)
Picture of the pandas dataframe I currently to make it easier for you
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 large file csv, it has 9600 columns and each column has a different type. When I read file using Dask Datafame and use attribute head(), I get error Mismatched dtypes found in pd.read_csv/pd.read_table. How can I ignore it. I use pandas read file csv don't have errors but very slow because the size of file is 2.5GB.
Thank!
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.