As you have observed, instantaneous signal amplitude is an easy to understand, one-dimensional quantity, whose averaged absolute value is loosely associated with perceived loudness, and which can be easily altered by multiplying your signal by a constant.
Frequency is a very different affair. If we lived in a world where the audio signals were made of sustained pure tones, it would make sense to ask "what is the frequency of this sound?". But things are more complicated:
- Stationary speech or music signals can be described as a sum of pure tones. Thus, it makes more sense to ask "what is the signal amplitude at this particular frequency?". This is why we look at spectra - graphs showing signal amplitude as a function of frequency. Note that frequency is not the plotted quantity, it is the $x$ axis!
- Speech or music signals are not stationary: some components of the signal decay over time or are modulated, some appears... Thus, it makes even more sense to ask "what is the signal amplitude at this particular frequency and particular time frame?". This is why we commonly see for audio analysis a representation called "spectrogram" - showing the measured signal energy as a function of time and frequency (again, note that frequency is the axis, not the measured quantity).
- In one of your comments you say "as well as be able to read the frequency data in each sample". Frequency is a rate of change, and to observe a change, we need a collection of samples - it makes no sense at all to do any frequency analysis on a single sample, and if you use Fourier analysis (which is just one of the ways of estimating a sum of sine waves from a signal), you'll need more and more samples to get a more and more precise resolution on the frequency axis. But remember that sounds change over time, and that performing a Fourier Transform discards timing information (the magnitude spectrum of two simultaneous 100 Hz and 300 Hz tones is the same as the spectrum of a 100 Hz tone followed in time by a 300 Hz tone). Thus, if you care about the temporal position of events, you will need to perform your analysis on slices of the signal, computing a FFT on overlapping blocks of the input signal - this is called the short-term Fourier transform (STFT). You'll have to deal with resolution problems here - the smaller your windows, the more accuracy you have on the time axis, the less accuracy you have on the frequency axis. The bigger the analysis windows, the less accuracy you have on the time axis, the more accuracy you have on the frequency axis. Again, and again, note that frequency is not what you estimate - your STFT is just answering the question "What is the signal energy in this particular frequency range and this particular time frame?".
I hope these will make you realize that there's no such thing as "the frequency" of an audio signal. Frequency is an axis, "where we look at things", not a measured quantity, "what we measure".
Note that there are some one-dimensional quantities homogeneous to frequencies (and thus expressed in Hz), that can be used to characterize stationary (stable) sounds. For generic audio or music, these quantities will vary over time:
- Fundamental frequency, is the inverse of the period of the sound. It relates to the perceived musical pitch (note) of the sound. This quantity is undefined for aperiodic sounds. Pitch-shifting algorithms will alter this quantity, to make a recording sound as if it was played/sung higher or lower on the musical scale.
- Roll-off frequency, is the frequency under which a given fraction (say 95%) of the energy of a sound is contained. This quantity can be affected by low-pass / high-pass filtering, to make a recording sound "brighter" or "duller". It is one of the many dimensions of the perceived timbre of sound.
Maybe some of these are the quantities you really care about?
One final word about the FFT - it is the hammer in a signal processing version of "if all you have is a hammer, everything looks like a nail". It is an extremely useful tool, but it is not the best fit for many audio signal processing/analysis problems, and more often than not it is used only as an intermediary processing or computational step. In particular, for some of the questions you might be interested in:
- Estimating the fundamental frequency might an involve a FFT, but the spectrum is not the right thing to look at, because of the "missing f0" phenomenon (a signal might be perceived as being a 263Hz C3 note, without having energy at this frequency). Don't make the mistake of trying to estimate the musical pitch of a signal by looking at the peak in its spectrum.
- Computing a FFT, messing with the numbers, and performing the inverse FFT is a poor way of filtering a signal. This should be done with digital filters, available in
scipy.signal.lfilter. You'll find formulae to compute IIR filter coefficients for a variety of musically useful second-order filters here.