Is there a pandas function for creating a stem and leaf plot

Question

Is there a pandas equivalent to R\'s stem() function for creating stem and leaf plots for a numeric Series?

I could easily write some code

Answer 1

Not to my knowledge, but it is not too hard to replicate:

eruptions = [3.6, 1.8, 3.333, 2.283, 4.533, 2.883, 4.7, 3.6, 1.95, 4.35, 1.833, 3.917, 4.2, 1.75, 4.7, 2.167, 1.75, 4.8, 1.6, 4.25, 1.8, 1.75, 3.45, 3.067, 4.533, 3.6, 1.967, 4.083, 3.85, 4.433, 4.3, 4.467, 3.367, 4.033, 3.833, 2.017, 1.867, 4.833, 1.833, 4.783, 4.35, 1.883, 4.567, 1.75, 4.533, 3.317, 3.833, 2.1, 4.633, 2.0, 4.8, 4.716, 1.833, 4.833, 1.733, 4.883, 3.717, 1.667, 4.567, 4.317, 2.233, 4.5, 1.75, 4.8, 1.817, 4.4, 4.167, 4.7, 2.067, 4.7, 4.033, 1.967, 4.5, 4.0, 1.983, 5.067, 2.017, 4.567, 3.883, 3.6, 4.133, 4.333, 4.1, 2.633, 4.067, 4.933, 3.95, 4.517, 2.167, 4.0, 2.2, 4.333, 1.867, 4.817, 1.833, 4.3, 4.667, 3.75, 1.867, 4.9, 2.483, 4.367, 2.1, 4.5, 4.05, 1.867, 4.7, 1.783, 4.85, 3.683, 4.733, 2.3, 4.9, 4.417, 1.7, 4.633, 2.317, 4.6, 1.817, 4.417, 2.617, 4.067, 4.25, 1.967, 4.6, 3.767, 1.917, 4.5, 2.267, 4.65, 1.867, 4.167, 2.8, 4.333, 1.833, 4.383, 1.883, 4.933, 2.033, 3.733, 4.233, 2.233, 4.533, 4.817, 4.333, 1.983, 4.633, 2.017, 5.1, 1.8, 5.033, 4.0, 2.4, 4.6, 3.567, 4.0, 4.5, 4.083, 1.8, 3.967, 2.2, 4.15, 2.0, 3.833, 3.5, 4.583, 2.367, 5.0, 1.933, 4.617, 1.917, 2.083, 4.583, 3.333, 4.167, 4.333, 4.5, 2.417, 4.0, 4.167, 1.883, 4.583, 4.25, 3.767, 2.033, 4.433, 4.083, 1.833, 4.417, 2.183, 4.8, 1.833, 4.8, 4.1, 3.966, 4.233, 3.5, 4.366, 2.25, 4.667, 2.1, 4.35, 4.133, 1.867, 4.6, 1.783, 4.367, 3.85, 1.933, 4.5, 2.383, 4.7, 1.867, 3.833, 3.417, 4.233, 2.4, 4.8, 2.0, 4.15, 1.867, 4.267, 1.75, 4.483, 4.0, 4.117, 4.083, 4.267, 3.917, 4.55, 4.083, 2.417, 4.183, 2.217, 4.45, 1.883, 1.85, 4.283, 3.95, 2.333, 4.15, 2.35, 4.933, 2.9, 4.583, 3.833, 2.083, 4.367, 2.133, 4.35, 2.2, 4.45, 3.567, 4.5, 4.15, 3.817, 3.917, 4.45, 2.0, 4.283, 4.767, 4.533, 1.85, 4.25, 1.983, 2.25, 4.75, 4.117, 2.15, 4.417, 1.817, 4.467]
waiting = [79, 54, 74, 62, 85, 55, 88, 85, 51, 85, 54, 84, 78, 47, 83, 52, 62, 84, 52, 79, 51, 47, 78, 69, 74, 83, 55, 76, 78, 79, 73, 77, 66, 80, 74, 52, 48, 80, 59, 90, 80, 58, 84, 58, 73, 83, 64, 53, 82, 59, 75, 90, 54, 80, 54, 83, 71, 64, 77, 81, 59, 84, 48, 82, 60, 92, 78, 78, 65, 73, 82, 56, 79, 71, 62, 76, 60, 78, 76, 83, 75, 82, 70, 65, 73, 88, 76, 80, 48, 86, 60, 90, 50, 78, 63, 72, 84, 75, 51, 82, 62, 88, 49, 83, 81, 47, 84, 52, 86, 81, 75, 59, 89, 79, 59, 81, 50, 85, 59, 87, 53, 69, 77, 56, 88, 81, 45, 82, 55, 90, 45, 83, 56, 89, 46, 82, 51, 86, 53, 79, 81, 60, 82, 77, 76, 59, 80, 49, 96, 53, 77, 77, 65, 81, 71, 70, 81, 93, 53, 89, 45, 86, 58, 78, 66, 76, 63, 88, 52, 93, 49, 57, 77, 68, 81, 81, 73, 50, 85, 74, 55, 77, 83, 83, 51, 78, 84, 46, 83, 55, 81, 57, 76, 84, 77, 81, 87, 77, 51, 78, 60, 82, 91, 53, 78, 46, 77, 84, 49, 83, 71, 80, 49, 75, 64, 76, 53, 94, 55, 76, 50, 82, 54, 75, 78, 79, 78, 78, 70, 79, 70, 54, 86, 50, 90, 54, 54, 77, 79, 64, 75, 47, 86, 63, 85, 82, 57, 82, 67, 74, 54, 83, 73, 73, 88, 80, 71, 83, 56, 79, 78, 84, 58, 83, 43, 60, 75, 81, 46, 90, 46, 74]
df = pd.DataFrame({'eruptions': eruptions, 'waiting': waiting})

s = sorted([str(round(i * 10, 1)) for i in df['eruptions']])
s = (
    pd.Series(s)
    .groupby([str(int(float(i) // 2 * 2)) for i in s])
    .apply(lambda group: "".join((x[-1] for x in group)))
    .reset_index()
)
print("\n".join(s['index'] +' | ' + s[0]))
# Output
# 16 | 070355555588
# 18 | 000022233333335577777777888822335777888
# 20 | 00002223378800035778
# 22 | 0002335578023578
# 24 | 00228
# 26 | 23
# 28 | 080
# 30 | 7
# 32 | 2337
# 34 | 250077
# 36 | 0000823577
# 38 | 2333335582225577
# 40 | 0000003357788888002233555577778
# 42 | 03335555778800233333555577778
# 44 | 02222335557780000000023333357778888
# 46 | 0000233357700000023578
# 48 | 00000022335800333
# 50 | 0370