Give column name when read csv file pandas
Question:
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
Answers:
I’d do it like this:
colnames=['TIME', 'X', 'Y', 'Z']
user1 = pd.read_csv('dataset/1.csv', names=colnames, header=None)
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)
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.
we can do it with a single line of code.
user1 = pd.read_csv('dataset/1.csv', names=['TIME', 'X', 'Y', 'Z'], header=None)
In addition to above answers if your dataset already has column names and you want to replace it with your custom names then set header=0 instead of header=None
Use nrows=1 to avoid consuming loading time if the dataset is too large.
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
I’d do it like this:
colnames=['TIME', 'X', 'Y', 'Z']
user1 = pd.read_csv('dataset/1.csv', names=colnames, header=None)
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)
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.
we can do it with a single line of code.
user1 = pd.read_csv('dataset/1.csv', names=['TIME', 'X', 'Y', 'Z'], header=None)
In addition to above answers if your dataset already has column names and you want to replace it with your custom names then set header=0 instead of header=None
Use nrows=1 to avoid consuming loading time if the dataset is too large.