Load data from txt with pandas
Question:
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?
Answers:
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.
@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"])
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 use this
import pandas as pd
dataset=pd.read_csv("filepath.txt",delimiter="t")
You can do as:
import pandas as pd
df = pd.read_csv('file_locationfilename.txt', delimiter = "t")
(like, df = pd.read_csv(‘F:Desktopdstext.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)
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
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")
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'])
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'])
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.
@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"])
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 use this
import pandas as pd
dataset=pd.read_csv("filepath.txt",delimiter="t")
You can do as:
import pandas as pd
df = pd.read_csv('file_locationfilename.txt', delimiter = "t")
(like, df = pd.read_csv(‘F:Desktopdstext.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)
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
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")
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'])
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'])