Python Dataframe Find the length of maximum element in a dataframe-CodePudding

I am trying to find the length (number of digits) in the maximum value of a data frame. Why? Then I know how much y-axis ticks will extend when plotting this data, and accordingly, I can adjust the plot's left border.

My code:

df = 
datetime                 A        B       C
2022-08-23 06:12:00     1.91    98.35   1.88
2022-08-23 06:13:00     1.92    92.04   1.77
2022-08-23 06:14:00     132.14  81.64   1.75

# maximum length element
max_len = df.round(2).dropna().astype('str').applymap(lambda x:len(x)).max().max()
print(max_len)
6

df.plot(figsize=(5,3),
        use_index=True,
        colormap=cm.get_cmap('Set1'),
        alpha=0.5)
# plot border for saving
left_border = (max_len/100) 0.05
plt.subplots_adjust(left=left_border, right=0.90, top=0.95, bottom=0.25)
plt.savefig(save_dir plot_df.index[i] '.jpg',dpi=500)
plt.show()

is there a better way to find the maximum length of the element?

CodePudding user response：

You can do this with the lambda, first find the max value in the dataframe, the cast to string and take len:

len(str(df.round(2).max().max()))
# Outputs: 6
%timeit returns: 979 µs ± 17 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

len(str(np.max(df.to_numpy())))
# Outputs: 6
%timeit returns: 9.35 µs ± 136 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Compared to your solution:

df.round(2).dropna().astype('str').applymap(lambda x:len(x)).max().max()
6
2.23 ms ± 68.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)