import pandas as pd
import numpy as np
# test data and dataframe
data = {0: {'NfL_BL': 5.67, 'NfL_V02': np.nan, 'NfL_V04': 6.15, 'NfL_V06': 7.94, 'NfL_V08': 9.03, 'NfL_V12': 40.200001},
5: {'NfL_BL': 7.88, 'NfL_V02': 6.66, 'NfL_V04': 7.1, 'NfL_V06': 8.19, 'NfL_V08': 8.39, 'NfL_V12': 8.57},
11: {'NfL_BL': 15.5, 'NfL_V02': np.nan, 'NfL_V04': 17.799999, 'NfL_V06': 19.799999, 'NfL_V08': 24.5, 'NfL_V12': 23.9},
16: {'NfL_BL': 6.52, 'NfL_V02': 6.38, 'NfL_V04': 7.22, 'NfL_V06': 8.98, 'NfL_V08': 8.0, 'NfL_V12': 7.35},
22: {'NfL_BL': 4.53, 'NfL_V02': np.nan, 'NfL_V04': 4.96, 'NfL_V06': 5.9, 'NfL_V08': 4.98, 'NfL_V12': 4.93}}
nfl = pd.DataFrame.from_dict(data, orient='index')
# display(nfl)
NfL_BL NfL_V02 NfL_V04 NfL_V06 NfL_V08 NfL_V12
0 5.67 NaN 6.15 7.94 9.03 40.20
5 7.88 6.66 7.10 8.19 8.39 8.57
11 15.50 NaN 17.80 19.80 24.50 23.90
16 6.52 6.38 7.22 8.98 8.00 7.35
22 4.53 NaN 4.96 5.90 4.98 4.93
# plot dataframe
nfl.plot()
# bar plot
nfl.plot.bar()
.transpose and plot- This will set the column headers as the index, and make it possible to plot them on the x-axis.
# transposed dataframe nfl.T
0 5 11 16 22
NfL_BL 5.67 7.88 15.5 6.52 4.53
NfL_V02 NaN 6.66 NaN 6.38 NaN
NfL_V04 6.15 7.10 17.8 7.22 4.96
NfL_V06 7.94 8.19 19.8 8.98 5.90
NfL_V08 9.03 8.39 24.5 8.00 4.98
NfL_V12 40.20 8.57 23.9 7.35 4.93
# plot a transposed dataframe
nfl.T.plot.bar()
For a histogram of all the values
- I recommend using seaborn, which is a high-level API for matplotlib.
- It makes working with different shapes of data, easier.
seaborn.distplot
import seaborn as sns
# plot
p = sns.distplot(a=nfl, kde=False)
p.set_xlabel('bins')
p.set_ylabel('counts')
