Import CSV file as a pandas DataFrame
PythonPandasCsvDataframePython Problem Overview
What's the Python way to read in a CSV file into a pandas DataFrame (which I can then use for statistical operations, can have differently-typed columns, etc.)?
My CSV file "value.txt" has the following content:
Date,"price","factor_1","factor_2"
2012-06-11,1600.20,1.255,1.548
2012-06-12,1610.02,1.258,1.554
2012-06-13,1618.07,1.249,1.552
2012-06-14,1624.40,1.253,1.556
2012-06-15,1626.15,1.258,1.552
2012-06-16,1626.15,1.263,1.558
2012-06-17,1626.15,1.264,1.572
In R we would read this file in using:
price <- read.csv("value.txt")
and that would return an R data.frame:
> price <- read.csv("value.txt")
> price
Date price factor_1 factor_2
1 2012-06-11 1600.20 1.255 1.548
2 2012-06-12 1610.02 1.258 1.554
3 2012-06-13 1618.07 1.249 1.552
4 2012-06-14 1624.40 1.253 1.556
5 2012-06-15 1626.15 1.258 1.552
6 2012-06-16 1626.15 1.263 1.558
7 2012-06-17 1626.15 1.264 1.572
Is there a Pythonic way to get the same functionality?
Python Solutions
Solution 1 - Python
pandas to the rescue:
import pandas as pd
print pd.read_csv('value.txt')
Date price factor_1 factor_2
0 2012-06-11 1600.20 1.255 1.548
1 2012-06-12 1610.02 1.258 1.554
2 2012-06-13 1618.07 1.249 1.552
3 2012-06-14 1624.40 1.253 1.556
4 2012-06-15 1626.15 1.258 1.552
5 2012-06-16 1626.15 1.263 1.558
6 2012-06-17 1626.15 1.264 1.572
This returns pandas DataFrame that is similar to R's.
Solution 2 - Python
To read a CSV file as a pandas DataFrame, you'll need to use pd.read_csv.
But this isn't where the story ends; data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly.
Here's a table listing common scenarios encountered with CSV files along with the appropriate argument you will need to use. You will usually need all or some combination of the arguments below to read in your data.
┌──────────────────────────────────────────────────────────┬─────────────────────────────┬────────────────────────────────────────────────────────┐
│ Scenario │ Argument │ Example │
├──────────────────────────────────────────────────────────┼─────────────────────────────┼────────────────────────────────────────────────────────┤
│ Read CSV with different separator¹ │ sep/delimiter │ read_csv(..., sep=';') │
│ Read CSV with tab/whitespace separator │ delim_whitespace │ read_csv(..., delim_whitespace=True) │
│ Fix UnicodeDecodeError while reading² │ encoding │ read_csv(..., encoding='latin-1') │
│ Read CSV without headers³ │ header and names │ read_csv(..., header=False, names=['x', 'y', 'z']) │
│ Specify which column to set as the index⁴ │ index_col │ read_csv(..., index_col=[0]) │
│ Read subset of columns │ usecols │ read_csv(..., usecols=['x', 'y']) │
│ Numeric data is in European format (eg., 1.234,56) │ thousands and decimal │ read_csv(..., thousands='.', decimal=',') │
└──────────────────────────────────────────────────────────┴─────────────────────────────┴────────────────────────────────────────────────────────┘
> Footnotes
>
> 1. By default, read_csv uses a C parser engine for performance. The C parser can only handle single character separators. If your CSV has
> a multi-character separator, you will need to modify your code to use
> the 'python' engine. You can also pass regular expressions:
>
> df = pd.read_csv(..., sep=r'\s*|\s*', engine='python')
>
> 2. UnicodeDecodeError occurs when the data was stored in one encoding format but read in a different, incompatible one. Most common
> encoding schemes are 'utf-8' and 'latin-1', your data is likely to
> fit into one of these.
>
> 3. header=False specifies that the first row in the CSV is a data row rather than a header row, and the names=[...] allows you to
> specify a list of column names to assign to the DataFrame when it is
> created.
>
> 4. "Unnamed: 0" occurs when a DataFrame with an un-named index is saved to CSV and then re-read after. Instead of having to fix the
> issue while reading, you can also fix the issue when writing by using
>
> df.to_csv(..., index=False)
There are other arguments I've not mentioned here, but these are the ones you'll encounter most frequently.
Solution 3 - Python
Here's an alternative to pandas library using Python's built-in csv module.
import csv
from pprint import pprint
with open('foo.csv', 'rb') as f:
reader = csv.reader(f)
headers = reader.next()
column = {h:[] for h in headers}
for row in reader:
for h, v in zip(headers, row):
column[h].append(v)
pprint(column) # Pretty printer
will print
{'Date': ['2012-06-11',
'2012-06-12',
'2012-06-13',
'2012-06-14',
'2012-06-15',
'2012-06-16',
'2012-06-17'],
'factor_1': ['1.255', '1.258', '1.249', '1.253', '1.258', '1.263', '1.264'],
'factor_2': ['1.548', '1.554', '1.552', '1.556', '1.552', '1.558', '1.572'],
'price': ['1600.20',
'1610.02',
'1618.07',
'1624.40',
'1626.15',
'1626.15',
'1626.15']}
Solution 4 - Python
import pandas as pd
df = pd.read_csv('/PathToFile.txt', sep = ',')
This will import your .txt or .csv file into a DataFrame.
Solution 5 - Python
Try this
import pandas as pd
data=pd.read_csv('C:/Users/Downloads/winequality-red.csv')
Replace the file target location, with where your data set is found, refer this url https://medium.com/@kanchanardj/jargon-in-python-used-in-data-science-to-laymans-language-part-one-12ddfd31592f
Solution 6 - Python
%cd C:\Users\asus\Desktop\python
import pandas as pd
df = pd.read_csv('value.txt')
df.head()
Date price factor_1 factor_2
0 2012-06-11 1600.20 1.255 1.548
1 2012-06-12 1610.02 1.258 1.554
2 2012-06-13 1618.07 1.249 1.552
3 2012-06-14 1624.40 1.253 1.556
4 2012-06-15 1626.15 1.258 1.552
Solution 7 - Python
You can use the csv module found in the python standard library to manipulate CSV files.
example:
import csv
with open('some.csv', 'rb') as f:
reader = csv.reader(f)
for row in reader:
print row
Solution 8 - Python
Note quite as clean, but:
import csv
with open("value.txt", "r") as f:
csv_reader = reader(f)
num = ' '
for row in csv_reader:
print num, '\t'.join(row)
if num == ' ':
num=0
num=num+1
Not as compact, but it does the job:
Date price factor_1 factor_2
1 2012-06-11 1600.20 1.255 1.548
2 2012-06-12 1610.02 1.258 1.554
3 2012-06-13 1618.07 1.249 1.552
4 2012-06-14 1624.40 1.253 1.556
5 2012-06-15 1626.15 1.258 1.552
6 2012-06-16 1626.15 1.263 1.558
7 2012-06-17 1626.15 1.264 1.572
Solution 9 - Python
import pandas as pd
dataset = pd.read_csv('/home/nspython/Downloads/movie_metadata1.csv')