Scikit-learn PCA .fit_transform shape is inconsistent (n_samples << m_attributes)

I am getting different shapes for my PCA using sklearn. Why isn't my transformation resulting in an array of the same dimensions like the docs say?

fit_transform(X, y=None)
Fit the model with X and apply the dimensionality reduction on X.
Parameters: 
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
Returns:    
X_new : array-like, shape (n_samples, n_components)

Check this out with the iris dataset which is (150, 4) where I'm making 4 PCs:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
import seaborn as sns; sns.set_style("whitegrid", {'axes.grid' : False})

%matplotlib inline
np.random.seed(0)

# Iris dataset
DF_data = pd.DataFrame(load_iris().data, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])],
                       columns = load_iris().feature_names)

Se_targets = pd.Series(load_iris().target, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])], 
                       name = "Species")

# Scaling mean = 0, var = 1
DF_standard = pd.DataFrame(StandardScaler().fit_transform(DF_data), 
                           index = DF_data.index,
                           columns = DF_data.columns)

# Sklearn for Principal Componenet Analysis

# Dims
m = DF_standard.shape[1]
K = m

# PCA (How I tend to set it up)
M_PCA = decomposition.PCA()
A_components = M_PCA.fit_transform(DF_standard)
#DF_standard.shape, A_components.shape
#((150, 4), (150, 4))

but then when I use the same exact approach on my actual dataset (76, 1989) as in 76 samples and 1989 attributes/dimensions I get a (76, 76) array instead of (76, 1989)

DF_centered = normalize(DF_mydata, method="center", axis=0)
m = DF_centered.shape[1]
# print(m)
# 1989
M_PCA = decomposition.PCA(n_components=m)
A_components = M_PCA.fit_transform(DF_centered)
DF_centered.shape, A_components.shape
# ((76, 1989), (76, 76))

normalize is just a wrapper I made that subtracts the mean from each dimension.

(Note: this answer is adapted from my answer on Cross Validated here: Why are there only n−1 principal components for n data points if the number of dimensions is larger or equal than n?)

PCA (as most typically run) creates a new coordinate system by:

1. shifting the origin to the centroid of your data,
2. squeezes and/or stretches the axes to make them equal in length, and
3. rotates your axes into a new orientation.

(For more details, see this excellent CV thread: Making sense of principal component analysis, eigenvectors & eigenvalues.) However, step 3 rotates your axes in a very specific way. Your new X1 (now called "PC1", i.e., the first principal component) is oriented in your data's direction of maximal variation. The second principal component is oriented in the direction of the next greatest amount of variation that is orthogonal to the first principal component. The remaining principal components are formed likewise.

With this in mind, let's examine a simple example (suggested by @amoeba in a comment). Here is a data matrix with two points in a three dimensional space:

X = [ 1 1 1 
      2 2 2 ]

Let's view these points in a (pseudo) three dimensional scatterplot:

So let's follow the steps listed above. (1) The origin of the new coordinate system will be located at (1.5,1.5,1.5). (2) The axes are already equal. (3) The first principal component will go diagonally from what used to be (0,0,0) to what was originally (3,3,3), which is the direction of greatest variation for these data. Now, the second principal component must be orthogonal to the first, and should go in the direction of the greatest remaining variation. But what direction is that? Is it from (0,0,3) to (3,3,0), or from (0,3,0) to (3,0,3), or something else? There is no remaining variation, so there cannot be any more principal components.

With N=2 data, we can fit (at most) N−1=1 principal components.