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: 17.03.2011
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, 14 2017 . 08:38 +

, , . - . -. , . . , noname , . , . , , .
, , python, pandas seaborn, .


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FilterMax Molecular Devices. , , . , , . 450 . . , . .
. . python pandas seaborn, pandas .
csv .
Roast,Compression,Absorbance
medium,level 1,21.31
medium,level 1,20.57
medium,level 1,24.49
medium,level 1,26.95
medium,level 1,20.49
medium,level 1,20.06
medium,level 1,21.22
medium,level 1,23.32
medium,level 2,28.09
medium,level 2,28.27
medium,level 2,23.13
medium,level 2,25.72
medium,level 2,26.75
medium,level 2,26.05
medium,level 2,26.92
medium,level 2,25.92
medium,level 3,32.88
medium,level 3,32.23
medium,level 3,33.13
medium,level 3,28.72
medium,level 3,28.82
medium,level 3,31.49
medium,level 3,32.31
medium,level 3,33.81
medium,level 4,38.68
medium,level 4,40.54
medium,level 4,39.34
medium,level 4,43.3
medium,level 4,41.48
medium,level 4,42.26
medium,level 4,42.73
medium,level 4,42.35
medium,level 5,57.62
medium,level 5,70.62
medium,level 5,70.74
medium,level 5,57.94
medium,level 5,77.62
medium,level 5,76.64
medium,level 5,69.12
medium,level 5,66.39
dark,level 1,27.54
dark,level 1,26.8
dark,level 1,30.72
dark,level 1,33.18
dark,level 1,26.72
dark,level 1,26.29
dark,level 1,27.45
dark,level 1,29.55
dark,level 2,34.32
dark,level 2,34.5
dark,level 2,29.36
dark,level 2,31.95
dark,level 2,32.98
dark,level 2,32.28
dark,level 2,33.15
dark,level 2,32.15
dark,level 3,39.11
dark,level 3,38.46
dark,level 3,39.36
dark,level 3,34.95
dark,level 3,35.05
dark,level 3,37.72
dark,level 3,38.54
dark,level 3,40.04
dark,level 4,44.91
dark,level 4,46.77
dark,level 4,45.57
dark,level 4,49.53
dark,level 4,47.71
dark,level 4,48.49
dark,level 4,48.96
dark,level 4,48.58
dark,level 5,63.85
dark,level 5,76.85
dark,level 5,76.97
dark,level 5,64.17
dark,level 5,83.85
dark,level 5,82.87
dark,level 5,75.35
dark,level 5,72.62


python, pandas seaborn


csv pandas dataframe. seaborn barplot , . Paired, . , , . , . , 450 32%. plt.axhline.


python
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns


df = pd.read_csv("data.csv")

sns.set()
sns.set_style("whitegrid")
sns.set_context("talk")

ax = sns.barplot(x="Compression", y="Absorbance", hue="Roast", data=df, palette="Paired")
ax.set(ylim=(0, 100), xlabel='Compression level', ylabel='Absorbance at 450 nm, %')
plt.axhline(32, alpha=0.4, color='black', linestyle='dashed', label='Optimal concentration')
plt.legend(loc='upper left')
plt.savefig("plot.png", dpi=300)
plt.show()



, , . , . , , .

, . Absorbance dataframe 32 ( ).
# Subtract optimal value
df['Absorbance'] = df['Absorbance'] - 32


python
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

df = pd.read_csv("data.csv")

# Subtract optimal value
df['Absorbance'] = df['Absorbance'] - 32

sns.set()
sns.set_style("whitegrid")
sns.set_context("talk")

ax = sns.barplot(x="Compression", y="Absorbance", hue="Roast", data=df, palette="Paired")
ax.set(xlabel='Compression level', ylabel='Deviation from optimal concentration')
plt.legend(loc='upper left')
plt.savefig("plot_diverging.png", dpi=300)
plt.show()



- , , )
P.S. , . , .
Original source: habrahabr.ru (comments, light).

https://habrahabr.ru/post/331754/

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