mnky9800n - 1 year ago 60

Python Question

I have three columns of data: two position values and one data value. I would like to pivot this data so that the elements of one column become the new columns and the elements of another one of the original columns become the indices. These data will be plotted using

`pcolormesh`

`pcolormesh`

`pcolormesh`

`pcolormesh`

The problem I have is that the code seems to remove data around

`x = 0.0`

I've added a plot (and hence some extra code) to give a visual aide to the problem statement. The left plot shows the original data, the right plot shows the result after the data has been reshaped for

`pcolormesh`

The code example I have provided should run in an ipython notebook by only copying and pasting.

Any suggestions are welcome. Perhaps this solution is super complicated? It sure feels that way.

`%matplotlib inline`

import decimal

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

test_df = pd.DataFrame()

test_df['x'] = [-2, -1.5, -0.9, -0.7, -0.5, 0.0, 0.5, 1.1]

test_df['y'] = [1,2,4,5,6,7,5,4]

test_df['v'] = np.random.randn(8)

def get_precision(number):

"""

gives the precision, or decimal place, of the number

http://stackoverflow.com/questions/6189956/easy-way-of-finding-decimal-places

"""

return int(abs(decimal.Decimal(str(number)).as_tuple().exponent))

def min_max(column):

column_min = np.floor(column.min())

column_max = np.ceil(column.max())

return column_min, column_max

def construct_df_for_pcolormesh(df, col, ix, values, columns_increment, index_increment):

columns_increment = 1.0/columns_increment

index_increment = 1.0/index_increment

columns_precision = get_precision(columns_increment)

index_precision = get_precision(index_increment)

columns_min, columns_max = min_max(df[col])

index_min, index_max = min_max(df[ix])

columns = np.linspace(columns_min, columns_max, (columns_max - columns_min)*columns_increment + 1)

index = np.linspace(index_min, index_max, (index_max - index_min)*index_increment + 1)

new_index = [(round(c, columns_precision), round(i, index_precision)) for c in columns for i in index]

df_for_pcolormesh = df.set_index([col, ix]).reindex(new_index).reset_index()

df_for_pcolormesh = df_for_pcolormesh.pivot(index=ix, columns=col, values=values)

return df_for_pcolormesh

fig, (ax,ax1)= plt.subplots(1,2, sharey=True, sharex=True)

test_df.plot(kind='scatter', x='x', y='y', s=100, grid=True, ax=ax)

ax.set_ylim(0,8)

ax.set_xlim(-2.5, 1.5)

ax.set_title('Plot with all the data')

data_df = construct_df_for_pcolormesh(test_df, 'x', 'y', 'v', 0.1, 0.1)

depths = data_df.index

xx = data_df.columns

d, x = np.meshgrid(depths, xx)

data = np.ma.masked_invalid(data_df.values)

ax1.pcolormesh(x, d, data.transpose(), cmap='viridis')

ax1.grid(True)

ax1.set_ylim(0,8)

ax1.set_xlim(-2.5, 1.5)

ax1.set_title('Plot with missing\ndatapoint at x=0.0')

Answer Source