How NumPy and pandas operate on CSV files
This article mainly introduces NumPy and pandas how to operate the CSV file related knowledge, the content is detailed and easy to understand, the operation is simple and fast, has a certain reference value, I believe that everyone after reading this NumPy and pandas how to operate the CSV file article will have a harvest, let's take a look at it.
The array is stored as a segregated file such as CSV:
Set the value of an array element to NaN:
In [26]: import numpy as np In [27]: np.random.seed (42) In [28]: a = np.random.randn (3Power4) In [29]: a [2] [2] = np.nan In [30]: print (a) [[0.49671415-0.1382643 0.64768854 1.52302986] [- 0.23415337-0.23413696 1.57921282 0.76743473] [- 0.46947439 0.54256004 nan-0.46572975]]
The savetxt () function of NumPy is a function corresponding to loadtxt (), which saves arrays in a partitioned file format such as CSV:
In [31]: np.savetxt ('np.csv',a,fmt='%.2f',delimiter=',',header= "# 1, 2, 3, 4")
In the function call above, we specify the name of the file to hold the array, the array, the optional format, the spacer, and an optional title
Through cat np.csv, you can view the specific contents of the np.csv file you just created.
Use random arrays to create pandas DataFrame:
In [38]: df = pd.DataFrame (a) In [39]: df Out [39]: 01230 0.496714-0.138264 0.647689 1.523030 1-0.234153-0.234137 1.579213 0.767435 2-0.469474 0.542560 NaN-0.465730
Pandas will automatically name the column for our data.
Using the to_csv () method of pandas, you can generate a DataFrame for the CSV file:
In [40]: df.to_csv ('pd.csv',float_format='%.2f',na_rep= "NAN!")
For this method, we need to provide a file name, an optional format string for the formatting parameters of the savetxt () function similar to NumPy, and an optional string that represents NaN
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