Quick way to visually explore data?
When you hit the limits of an application like Apple Numbers, or Excel, you need to start using a programming language like Python, R or C. Using a programming language, you write your own application, which is not constrained by arbitrary limits like 220 rows by 214 columns. (Of course, you are still constrained by the physical memory and the way that a particular language addresses it: R and Python tend to be more resource-intensive than C).

Let me give you an example using Python. You can install Python for your system from the official site. If you install one of the latest versions, you will already get pip as an installer of further Python packages. Install Pandas by starting a terminal (on OS X, there is a 'Terminal' application, on Windows this used to be a MS-DOS window ...), and typing into that terminal:

pip install pandas


You can then start Python in an interactive session by typing python in a terminal. In the Python shell, you can read the data as mentioned in @tasos 's answer:

&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; df = pd.read_csv('name_of_your_file.csv')


Here you can use the describe method of the df DataFrame to get some statistics about the read data. These numbers are for a randomly generated data frame:

&gt;&gt;&gt; df.describe()
          0         1         2
count  3.000000  3.000000  3.000000
mean  -0.150869  0.444380 -0.117066
std    0.751421  0.697880  0.565328
min   -1.017424 -0.356764 -0.506030
25%   -0.386514  0.206466 -0.441313
50%    0.244396  0.769696 -0.376595
75%    0.282409  0.844952  0.077416
max    0.320422  0.920208  0.531427


Then you can generate plots for different columns, one against the other, using the Pandas plotting functions. Check the many examples in the linked documentation. Here is a small example from there:

import matplotlib.pyplot as plt
import pandas as pd
import bumpy as np

df = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
                   'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
plt.figure()
df.plot(kind='hist', alpha=0.5)


Usually, you will need to use another call more in order to save it to a file:

plt.save('output.png')  ## or *.svg, *.pdf


In the file you should find something similar to this figure.