python - How do I shift categorical scatter markers to left and right above xticks (multiple data sets per category)? -


i have simple pandas dataframe want plot matplotlib:

import pandas pd import matplotlib.pyplot plt  df = pd.read_excel('sat_data.xlsx', index_col = 'state')  plt.figure() plt.scatter(df['year'], df['reading'], c = 'blue', s = 25) plt.scatter(df['year'], df['math'], c = 'orange', s = 25) plt.scatter(df['year'], df['writing'], c = 'red', s = 25)

here plot looks like:

plot of data

i'd shift blue data points bit left, , red ones bit right, each year on x-axis has 3 mini-columns of scatter data above instead of 3 datasets overlapping. tried , failed use 'verts' argument properly. there better way this?

a quick , dirty way create small offset dx , subtract x values of blue points , add x values of red points. 

dx = 0.1 plt.scatter(df['year'] - dx, df['reading'], c = 'blue', s = 25)  plt.scatter(df['year'],      df['math'], c = 'orange', s = 25)  plt.scatter(df['year'] + dx, df['writing'], c = 'red', s = 25)

one more option use stripplot function seaborn library. necessary melt original dataframe long form each row contains year, test , score. make stripplot specifying year x, score y , test hue. split keyword argument controls plotting categories separate stripes each x. there's jitter argument add noise x values take small area instead of being on single vertical line.

import pandas pd import seaborn sns  # make example data np.random.seed(2017) df = pd.dataframe(columns = ['reading','math','writing'],                    data = np.random.normal(540,30,size=(1000,3))) df['year'] = np.random.choice(np.arange(2006,2016),size=1000)  # melt data long form df1 = pd.melt(df, var_name='test', value_name='score',id_vars=['year'])  # make stripplot fig, ax = plt.subplots(figsize=(10,7)) sns.stripplot(data = df1, x='year', y = 'score', hue = 'test',                jitter = true, split = true, alpha = 0.7,                palette = ['blue','orange','red'])

output:

enter image description here


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