This is the example of my dataset.
>>> user1 = pd.read_csv('dataset/1.csv')
>>> print(user1)
0 0.69464 3.1735 7.5048
0 0.030639 0.14982 3.48680 9.2755
1 0.069763 -0.29965 1.94770 9.1120
2 0.099823 -1.68890 1.41650 10.1200
3 0.129820 -2.17930 0.95342 10.9240
4 0.159790 -2.30180 0.23155 10.6510
5 0.189820 -1.41650 1.18500 11.0730
How to push down the first column and add the names column [TIME, X, Y, and Z] on the first column.
The desired output is like this:
TIME X Y Z
0 0 0.69464 3.1735 7.5048
1 0.030639 0.14982 3.48680 9.2755
2 0.069763 -0.29965 1.94770 9.1120
3 0.099823 -1.68890 1.41650 10.1200
4 0.129820 -2.17930 0.95342 10.9240
5 0.159790 -2.30180 0.23155 10.6510
6 0.189820 -1.41650 1.18500 11.0730
user1 = pd.read_csv('dataset/1.csv', names=['Time', 'X', 'Y', 'Z'])
names parameter in read_csv function is used to define column names. If you pass extra name in this list, it will add another new column with that name with NaN values.
header=None is used to trim column names is already exists in CSV file.
If we are directly use data from csv it will give combine data based on comma separation value as it is .csv file.
user1 = pd.read_csv('dataset/1.csv')
If you want to add column names using pandas, you have to do something like this. But below code will not show separate header for your columns.
col_names=['TIME', 'X', 'Y', 'Z']
user1 = pd.read_csv('dataset/1.csv', names=col_names)
To solve above problem we have to add extra filled which is supported by pandas, It is header=None
user1 = pd.read_csv('dataset/1.csv', names=col_names, header=None)
we can do it with a single line of code.
user1 = pd.read_csv('dataset/1.csv', names=['TIME', 'X', 'Y', 'Z'], header=None)
Source: Stackoverflow.com