# How do I read CSV data into a record array in NumPy?

## Question:

Is there a direct way to import the contents of a CSV file into a record array, just like how R’s `read.table()`

, `read.delim()`

, and `read.csv()`

import data into R dataframes?

Or should I use `csv.reader()`

and then apply `numpy.core.records.fromrecords()`

?

## Answers:

Use `numpy.genfromtxt()`

by setting the `delimiter`

kwarg to a comma:

```
from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')
```

You can also try `recfromcsv()`

which can guess data types and return a properly formatted record array.

Use `pandas.read_csv`

:

```
import pandas as pd
df = pd.read_csv('myfile.csv', sep=',', header=None)
print(df.values)
```

```
array([[ 1. , 2. , 3. ],
[ 4. , 5.5, 6. ]])
```

This gives a pandas `DataFrame`

which provides many useful data manipulation functions which are not directly available with numpy record arrays.

`DataFrame`

is a 2-dimensional labeled data structure with columns of

potentially different types. You can think of it like a spreadsheet or

SQL table…

I would also recommend `numpy.genfromtxt`

. However, since the question asks for a record array, as opposed to a normal array, the `dtype=None`

parameter needs to be added to the `genfromtxt`

call:

```
import numpy as np
np.genfromtxt('myfile.csv', delimiter=',')
```

For the following `'myfile.csv'`

:

```
1.0, 2, 3
4, 5.5, 6
```

the code above gives an array:

```
array([[ 1. , 2. , 3. ],
[ 4. , 5.5, 6. ]])
```

and

```
np.genfromtxt('myfile.csv', delimiter=',', dtype=None)
```

gives a record array:

```
array([(1.0, 2.0, 3), (4.0, 5.5, 6)],
dtype=[('f0', '<f8'), ('f1', '<f8'), ('f2', '<i4')])
```

This has the advantage that files with multiple data types (including strings) can be easily imported.

I tried it :

```
from numpy import genfromtxt
genfromtxt(fname = dest_file, dtype = (<whatever options>))
```

versus :

```
import csv
import numpy as np
with open(dest_file,'r') as dest_f:
data_iter = csv.reader(dest_f,
delimiter = delimiter,
quotechar = '"')
data = [data for data in data_iter]
data_array = np.asarray(data, dtype = <whatever options>)
```

on 4.6 million rows with about 70 columns and found that the NumPy path took 2 min 16 secs and the csv-list comprehension method took 13 seconds.

I would recommend the csv-list comprehension method as it is most likely relies on pre-compiled libraries and not the interpreter as much as NumPy. I suspect the pandas method would have similar interpreter overhead.

You can use this code to send CSV file data into an array:

```
import numpy as np
csv = np.genfromtxt('test.csv', delimiter=",")
print(csv)
```

I tried this:

```
import pandas as p
import numpy as n
closingValue = p.read_csv("<FILENAME>", usecols=[4], dtype=float)
print(closingValue)
```

As I tried both ways using NumPy and Pandas, using pandas has a lot of advantages:

- Faster
- Less CPU usage
- 1/3 RAM usage compared to NumPy genfromtxt

This is my test code:

```
$ for f in test_pandas.py test_numpy_csv.py ; do /usr/bin/time python $f; done
2.94user 0.41system 0:03.05elapsed 109%CPU (0avgtext+0avgdata 502068maxresident)k
0inputs+24outputs (0major+107147minor)pagefaults 0swaps
23.29user 0.72system 0:23.72elapsed 101%CPU (0avgtext+0avgdata 1680888maxresident)k
0inputs+0outputs (0major+416145minor)pagefaults 0swaps
```

### test_numpy_csv.py

```
from numpy import genfromtxt
train = genfromtxt('/home/hvn/me/notebook/train.csv', delimiter=',')
```

### test_pandas.py

```
from pandas import read_csv
df = read_csv('/home/hvn/me/notebook/train.csv')
```

### Data file:

```
du -h ~/me/notebook/train.csv
59M /home/hvn/me/notebook/train.csv
```

With NumPy and pandas at versions:

```
$ pip freeze | egrep -i 'pandas|numpy'
numpy==1.13.3
pandas==0.20.2
```

Using `numpy.loadtxt`

A quite simple method. But it requires all the elements being float (int and so on)

```
import numpy as np
data = np.loadtxt('c:\1.csv',delimiter=',',skiprows=0)
```

This is the easiest way:

```
import csv
with open('testfile.csv', newline='') as csvfile:
data = list(csv.reader(csvfile))
```

Now each entry in data is a record, represented as an array. So you have a 2D array. It saved me so much time.

I would suggest using tables (`pip3 install tables`

). You can save your `.csv`

file to `.h5`

using pandas (`pip3 install pandas`

),

```
import pandas as pd
data = pd.read_csv("dataset.csv")
store = pd.HDFStore('dataset.h5')
store['mydata'] = data
store.close()
```

You can then easily, and with less time even for huge amount of data, load your data in a *NumPy array*.

```
import pandas as pd
store = pd.HDFStore('dataset.h5')
data = store['mydata']
store.close()
# Data in NumPy format
data = data.values
```

**This work as a charm…**

```
import csv
with open("data.csv", 'r') as f:
data = list(csv.reader(f, delimiter=";"))
import numpy as np
data = np.array(data, dtype=np.float)
```

```
In [329]: %time my_data = genfromtxt('one.csv', delimiter=',')
CPU times: user 19.8 s, sys: 4.58 s, total: 24.4 s
Wall time: 24.4 s
In [330]: %time df = pd.read_csv("one.csv", skiprows=20)
CPU times: user 1.06 s, sys: 312 ms, total: 1.38 s
Wall time: 1.38 s
```

Available on the newest pandas and numpy version.

```
import pandas as pd
import numpy as np
data = pd.read_csv('data.csv', header=None)
# Discover, visualize, and preprocess data using pandas if needed.
data = data.to_numpy()
```

this is a very simple task, the best way to do this is as follows

```
import pandas as pd
import numpy as np
df = pd.read_csv(r'C:UsersRonDesktopClients.csv') #read the file (put 'r' before the path string to address any special characters in the file such as ). Don't forget to put the file name at the end of the path + ".csv"
print(df)`
y = np.array(df)
```