Near-field acoustic source localization and beamforming has hitherto not been investigated extensively in the spherical harmonic domain under reverberant conditions. In this paper, a novel method for the near-field direction of arrival (DOA) and range estimation using signal invariant and direction independent spherical harmonic features is proposed. A spatial pressure interpolation method that effectively captures the acoustic energy on the surface of the sphere is first developed in the spherical harmonic domain. Near-field DOA estimates are then computed using this pressure distribution. Spherical harmonic features that are signal invariant and direction independent are then extracted using the near field DOA estimates. Signal invariant features are obtained by normalizing spherical harmonic coefficients with a component that is proportional to the source signal strength. Direction independent features are obtained using two methods. Rotation of spherical harmonic functions over a sphere is performed using Wigner-D functions in one method, whereas in the other, the effect of DOA is compensated by spherical harmonic normalization. Using the signal invariant and direction independent features, a learning-based framework which utilizes a convolutional neural network and voicing activity detection is also developed to compute the range of the near-field source. Experiments are conducted both on simulated and real speech data for evaluating the performance of the proposed spherical harmonic features in the context of near-field localization as well as beamforming. Root mean square error of both near-field DOA and source range estimates are obtained. Objective evaluation of near-field beamformed acoustic outputs is also performed. Results obtained are motivating enough for the method to be used in practical near-field beamforming applications.