Collection of useful helper methods for interactive data science work in python. Usually on jupyter notebooks, using the basic python scientific stack.
MIT License
The library is available on pip:
pip install dsutil
For full usage examples see notebooks under the examples directory.
Full examples for plotting here
add_grid()
: reasonable default grid settings, with weak grey lines, light alpha, etc.
import numpy as np
import matplotlib.pyplot as plt
from dsutil.plotting import add_grid
x = np.linspace(0.0,100,10)
y = np.random.uniform(low=0,high=10,size=10)
plt.bar(x,y)
add_grid()
add_value_labels()
annotates barplots, line plots and scatter plots with values for the coordinates
import numpy as np
import matplotlib.pyplot as plt
from dsutil.plotting import add_value_labels
x = np.linspace(0.0,100,10)
y = np.random.uniform(low=0,high=10,size=10)
plt.bar(x,y)
add_value_labels()
format_yaxis_percentage()
: turns values between 0 and 1 in y-axis into percentages
import numpy as np
import matplotlib.pyplot as plt
from dsutil.plotting import format_yaxis_percentage
x = np.linspace(0.0,100,10)
y = np.random.uniform(low=0,high=1,size=10)
plt.bar(x,y)
plt.yticks(np.arange(0,1.01,0.1))
format_yaxis_percentage()
format_yaxis_thousands()
: uses commas as thousands separator in the y-axis labels
import numpy as np
import matplotlib.pyplot as plt
from dsutil.plotting import format_yaxis_thousands
x = np.linspace(0.0,100,10)
y = np.random.uniform(low=10000,high=100000,size=10)
plt.bar(x,y)
plt.yticks(np.arange(0,100001,10000))
format_yaxis_thousands()