How to plot columns of dictionaries containing a numpy array
Question:
I have a dataframe with a dict inside each cell that contains data like the pic attached, I want to plot column 1 and 2 (index order) to get something like this:
n = len(df3)
fig,ax = plt.subplots()
for i in range(n):
ax.plot([*df3[i][:,1].values()], [*df3[i][:,2].values()],label=df3[i][:,0])
plt.show()
I tried the code below but isnt working, any guide ples thanks!!
Here is the pk file:
https://www.mediafire.com/file/6zzzrd01k3hyywr/df3result.pk/file
Sample data
data =
{'Time': [pd.Timestamp('2018-01-27 00:00:00'), pd.Timestamp('2018-05-07 00:00:00'), pd.Timestamp('2018-08-15 00:00:00'), pd.Timestamp('2018-11-23 00:00:00'), pd.Timestamp('2019-03-03 00:00:00')], 'Liqwt': [{'Liqwt': np.array([ 18.1123, 969.674 , 1921.23 , 2872.8 , 3824.36 ,
4775.92 , 5727.48 , 6679.04 , 7630.6 , 8582.17 ,
9533.73 , 10485.3 , 11436.8 , 12388.4 , 13340. ,
14291.5 , 15243.1 , 16194.7 , 17146.2 , 18097.8 ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'Liqwt': np.array([ 38.0128, 2035.08 , 4032.16 , 6029.23 , 8026.3 ,
10023.4 , 12020.4 , 14017.5 , 16014.6 , 18011.7 ,
20008.7 , 22005.8 , 24002.9 , 25999.9 , 27997. ,
29994.1 , 31991.2 , 33988.2 , 35985.3 , 37982.4 ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}], 'IPRwt': [{'IPRwt': np.array([3919.98 , 3782.97 , 3641.74 , 3496.72 , 3345.06 , 3185.46 ,
3019.55 , 2846.7 , 2667.38 , 2482.27 , 2289.33 , 2087.15 ,
1874.18 , 1647.9 , 1403.7 , 1127.59 , 812.477, 436.561,
0. , 0. ])}, {'IPRwt': np.array([3823.82 , 3708.6 , 3578.81 , 3441.95 , 3299.97 , 3153.51 ,
3002.86 , 2848.15 , 2689.46 , 2526.85 , 2360.33 , 2190.32 ,
2017. , 1839.47 , 1656.83 , 1469.83 , 1276.69 , 1073.84 ,
854.08 , 600.952])}, {'IPRwt': np.array([ 3.74833e+03, 3.61396e+03, 3.46258e+03, 3.30363e+03,
3.13850e+03, 2.96272e+03, 2.77724e+03, 2.58063e+03,
2.37299e+03, 2.15353e+03, 1.92058e+03, 1.66852e+03,
1.38785e+03, 1.05660e+03, 7.01630e+02, 1.92702e+02,
0.00000e+00, 0.00000e+00, 0.00000e+00, -1.00000e+00])}, {'IPRwt': np.array([3722. , 3637.19 , 3522.64 , 3392.73 , 3250.45 , 3097.7 ,
2935.76 , 2763.18 , 2580.25 , 2386.64 , 2179.46 , 1955.4 ,
1709.25 , 1430.54 , 1112.86 , 780.77 , 327.827, 0. ,
0. , 0. ])}, {'IPRwt': np.array([3581.5 , 3486.05 , 3372.4 , 3250.96 , 3123.71 , 2991.04 ,
2852.21 , 2706.23 , 2552.53 , 2389.3 , 2216.65 , 2033.92 ,
1839.21 , 1629.9 , 1400.3 , 1138.24 , 838.932, 514.519,
0. , 0. ])}], 'LiqNod': [{'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 38.0128, 2035.08 , 4032.16 , 6029.23 , 8026.3 ,
10023.4 , 12020.4 , 14017.5 , 16014.6 , 18011.7 ,
20008.7 , 22005.8 , 24002.9 , 25999.9 , 27997. ,
29994.1 , 31991.2 , 33988.2 , 35985.3 , 37982.4 ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}], 'IPRNod': [{'IPRNod': np.array([4970.64, 4842.67, 4714.7 , 4586.73, 4458.76, 4330.79, 4202.82,
4074.85, 3946.88, 3818.9 , 3690.93, 3562.96, 3434.99, 3307.02,
3179.05, 3051.08, 2923.11, 2795.14, 2667.17, 2539.2 ])}, {'IPRNod': np.array([4862.36 , 4703.68 , 4545.01 , 4386.33 , 4227.48 , 4064.74 ,
3896.13 , 3720.94 , 3538.32 , 3347.22 , 3146.31 , 2933.89 ,
2707.67 , 2464.52 , 2199.87 , 1906.67 , 1572.86 , 1174.34 ,
645.351, -261.368])}, {'IPRNod': np.array([4728.5 , 4609.39, 4488.23, 4364.17, 4236.96, 4106.37, 3972.09,
3833.79, 3691.08, 3543.5 , 3390.5 , 3231.42, 3065.47, 2891.66,
2708.71, 2515.01, 2308.36, 2085.72, 1842.6 , 1571.95])}, {'IPRNod': np.array([4647.81, 4532.68, 4417.56, 4302.43, 4187.31, 4072.18, 3957.06,
3841.57, 3723.42, 3601.99, 3476.97, 3348.02, 3214.72, 3076.57,
2933. , 2783.26, 2626.47, 2461.49, 2286.82, 2100.47])}, {'IPRNod': np.array([4539.7 , 4402.95 , 4266.19 , 4129.43 , 3992.67 , 3855.73 ,
3715.47 , 3570.37 , 3419.87 , 3263.34 , 3099.99 , 2928.84 ,
2748.64 , 2557.8 , 2354.16 , 2134.76 , 1895.23 , 1628.76 ,
1323.48 , 954.945])}]}
df = pd.DataFrame(data)
# display(df)
Time Liqwt IPRwt LiqNod IPRNod
0 2018-01-27 {'Liqwt': [18.1123, 969.674, 1921.23, 2872.8, 3824.36, 4775.92, 5727.48, 6679.04, 7630.6, 8582.17, 9533.73, 10485.3, 11436.8, 12388.4, 13340.0, 14291.5, 15243.1, 16194.7, 17146.2, 18097.8]} {'IPRwt': [3919.98, 3782.97, 3641.74, 3496.72, 3345.06, 3185.46, 3019.55, 2846.7, 2667.38, 2482.27, 2289.33, 2087.15, 1874.18, 1647.9, 1403.7, 1127.59, 812.477, 436.561, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4970.64, 4842.67, 4714.7, 4586.73, 4458.76, 4330.79, 4202.82, 4074.85, 3946.88, 3818.9, 3690.93, 3562.96, 3434.99, 3307.02, 3179.05, 3051.08, 2923.11, 2795.14, 2667.17, 2539.2]}
1 2018-05-07 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3823.82, 3708.6, 3578.81, 3441.95, 3299.97, 3153.51, 3002.86, 2848.15, 2689.46, 2526.85, 2360.33, 2190.32, 2017.0, 1839.47, 1656.83, 1469.83, 1276.69, 1073.84, 854.08, 600.952]} {'LiqNod': [38.0128, 2035.08, 4032.16, 6029.23, 8026.3, 10023.4, 12020.4, 14017.5, 16014.6, 18011.7, 20008.7, 22005.8, 24002.9, 25999.9, 27997.0, 29994.1, 31991.2, 33988.2, 35985.3, 37982.4]} {'IPRNod': [4862.36, 4703.68, 4545.01, 4386.33, 4227.48, 4064.74, 3896.13, 3720.94, 3538.32, 3347.22, 3146.31, 2933.89, 2707.67, 2464.52, 2199.87, 1906.67, 1572.86, 1174.34, 645.351, -261.368]}
2 2018-08-15 {'Liqwt': [38.0128, 2035.08, 4032.16, 6029.23, 8026.3, 10023.4, 12020.4, 14017.5, 16014.6, 18011.7, 20008.7, 22005.8, 24002.9, 25999.9, 27997.0, 29994.1, 31991.2, 33988.2, 35985.3, 37982.4]} {'IPRwt': [3748.33, 3613.96, 3462.58, 3303.63, 3138.5, 2962.72, 2777.24, 2580.63, 2372.99, 2153.53, 1920.58, 1668.52, 1387.85, 1056.6, 701.63, 192.702, 0.0, 0.0, 0.0, -1.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4728.5, 4609.39, 4488.23, 4364.17, 4236.96, 4106.37, 3972.09, 3833.79, 3691.08, 3543.5, 3390.5, 3231.42, 3065.47, 2891.66, 2708.71, 2515.01, 2308.36, 2085.72, 1842.6, 1571.95]}
3 2018-11-23 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3722.0, 3637.19, 3522.64, 3392.73, 3250.45, 3097.7, 2935.76, 2763.18, 2580.25, 2386.64, 2179.46, 1955.4, 1709.25, 1430.54, 1112.86, 780.77, 327.827, 0.0, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4647.81, 4532.68, 4417.56, 4302.43, 4187.31, 4072.18, 3957.06, 3841.57, 3723.42, 3601.99, 3476.97, 3348.02, 3214.72, 3076.57, 2933.0, 2783.26, 2626.47, 2461.49, 2286.82, 2100.47]}
4 2019-03-03 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3581.5, 3486.05, 3372.4, 3250.96, 3123.71, 2991.04, 2852.21, 2706.23, 2552.53, 2389.3, 2216.65, 2033.92, 1839.21, 1629.9, 1400.3, 1138.24, 838.932, 514.519, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4539.7, 4402.95, 4266.19, 4129.43, 3992.67, 3855.73, 3715.47, 3570.37, 3419.87, 3263.34, 3099.99, 2928.84, 2748.64, 2557.8, 2354.16, 2134.76, 1895.23, 1628.76, 1323.48, 954.945]}
Answers:
- In the case of this data, the
'Time' column is datetime64[ns] Dtype, and the columns to be plotted, contain rows of dict with a np.array, the columns don’t contain string literals.
- Extract all the arrays from the dictionaries
- Use
.apply with .map(dict.values), and pd.Series.explode
.apply(lambda x: x.map(dict.values)) produces:
dict_values([array([ 18.1123, 969.674 , ... , 17146.2 , 18097.8 ])])
- Each
array is then extracted from dict_values(...) with .apply(pd.Series.explode).
- Create colors for the plots, because the standard color cycler doesn’t have enough unique colors. The easiest way is the use
sns.color_palette. seaborn is a high-level API for matplotlib.
- See creating over 20 unique legend colors using matplotlib for other alternatives.
- Choosing Colormaps in Matplotlib & Choosing color palettes in seaborn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# load the data from the file, or use the sample df in the OP
df = pd.read_pickle('df3result.pk')
# 1.
df.loc[:, ['Liqwt', 'IPRwt', 'LiqNod', 'IPRNod']] =
df.loc[:, ['Liqwt', 'IPRwt', 'LiqNod', 'IPRNod']].apply(lambda x: x.map(dict.values)).apply(pd.Series.explode)
# 2.
colors = sns.color_palette('husl', n_colors=len(df))
# plot
plt.rcParams.update({'font.size': 16})
fig, ax = plt.subplots(figsize=(12, 9))
# iterate through each row of the dataframe, zipped to the color for that row
for (_, (time, liqwt, iprwt)), color in zip(df[['Time', 'Liqwt', 'IPRwt']].iterrows(), colors):
ax.plot(liqwt, iprwt, '.-', color=color, label=time.date())
ax.set(xlabel='Oil Rate, BOPD', ylabel='$P_{wf},psi$')
ax.legend(bbox_to_anchor=(1, 1.02), loc='upper left', frameon=False)
plt.show()
I have a dataframe with a dict inside each cell that contains data like the pic attached, I want to plot column 1 and 2 (index order) to get something like this:
n = len(df3)
fig,ax = plt.subplots()
for i in range(n):
ax.plot([*df3[i][:,1].values()], [*df3[i][:,2].values()],label=df3[i][:,0])
plt.show()
I tried the code below but isnt working, any guide ples thanks!!
Here is the pk file:
https://www.mediafire.com/file/6zzzrd01k3hyywr/df3result.pk/file
Sample data
data =
{'Time': [pd.Timestamp('2018-01-27 00:00:00'), pd.Timestamp('2018-05-07 00:00:00'), pd.Timestamp('2018-08-15 00:00:00'), pd.Timestamp('2018-11-23 00:00:00'), pd.Timestamp('2019-03-03 00:00:00')], 'Liqwt': [{'Liqwt': np.array([ 18.1123, 969.674 , 1921.23 , 2872.8 , 3824.36 ,
4775.92 , 5727.48 , 6679.04 , 7630.6 , 8582.17 ,
9533.73 , 10485.3 , 11436.8 , 12388.4 , 13340. ,
14291.5 , 15243.1 , 16194.7 , 17146.2 , 18097.8 ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'Liqwt': np.array([ 38.0128, 2035.08 , 4032.16 , 6029.23 , 8026.3 ,
10023.4 , 12020.4 , 14017.5 , 16014.6 , 18011.7 ,
20008.7 , 22005.8 , 24002.9 , 25999.9 , 27997. ,
29994.1 , 31991.2 , 33988.2 , 35985.3 , 37982.4 ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'Liqwt': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}], 'IPRwt': [{'IPRwt': np.array([3919.98 , 3782.97 , 3641.74 , 3496.72 , 3345.06 , 3185.46 ,
3019.55 , 2846.7 , 2667.38 , 2482.27 , 2289.33 , 2087.15 ,
1874.18 , 1647.9 , 1403.7 , 1127.59 , 812.477, 436.561,
0. , 0. ])}, {'IPRwt': np.array([3823.82 , 3708.6 , 3578.81 , 3441.95 , 3299.97 , 3153.51 ,
3002.86 , 2848.15 , 2689.46 , 2526.85 , 2360.33 , 2190.32 ,
2017. , 1839.47 , 1656.83 , 1469.83 , 1276.69 , 1073.84 ,
854.08 , 600.952])}, {'IPRwt': np.array([ 3.74833e+03, 3.61396e+03, 3.46258e+03, 3.30363e+03,
3.13850e+03, 2.96272e+03, 2.77724e+03, 2.58063e+03,
2.37299e+03, 2.15353e+03, 1.92058e+03, 1.66852e+03,
1.38785e+03, 1.05660e+03, 7.01630e+02, 1.92702e+02,
0.00000e+00, 0.00000e+00, 0.00000e+00, -1.00000e+00])}, {'IPRwt': np.array([3722. , 3637.19 , 3522.64 , 3392.73 , 3250.45 , 3097.7 ,
2935.76 , 2763.18 , 2580.25 , 2386.64 , 2179.46 , 1955.4 ,
1709.25 , 1430.54 , 1112.86 , 780.77 , 327.827, 0. ,
0. , 0. ])}, {'IPRwt': np.array([3581.5 , 3486.05 , 3372.4 , 3250.96 , 3123.71 , 2991.04 ,
2852.21 , 2706.23 , 2552.53 , 2389.3 , 2216.65 , 2033.92 ,
1839.21 , 1629.9 , 1400.3 , 1138.24 , 838.932, 514.519,
0. , 0. ])}], 'LiqNod': [{'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 38.0128, 2035.08 , 4032.16 , 6029.23 , 8026.3 ,
10023.4 , 12020.4 , 14017.5 , 16014.6 , 18011.7 ,
20008.7 , 22005.8 , 24002.9 , 25999.9 , 27997. ,
29994.1 , 31991.2 , 33988.2 , 35985.3 , 37982.4 ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}, {'LiqNod': np.array([ 40. , 2141.47, 4242.95, 6344.42, 8445.89, 10547.4 ,
12648.8 , 14750.3 , 16851.8 , 18953.3 , 21054.7 , 23156.2 ,
25257.7 , 27359.2 , 29460.6 , 31562.1 , 33663.6 , 35765. ,
37866.5 , 39968. ])}], 'IPRNod': [{'IPRNod': np.array([4970.64, 4842.67, 4714.7 , 4586.73, 4458.76, 4330.79, 4202.82,
4074.85, 3946.88, 3818.9 , 3690.93, 3562.96, 3434.99, 3307.02,
3179.05, 3051.08, 2923.11, 2795.14, 2667.17, 2539.2 ])}, {'IPRNod': np.array([4862.36 , 4703.68 , 4545.01 , 4386.33 , 4227.48 , 4064.74 ,
3896.13 , 3720.94 , 3538.32 , 3347.22 , 3146.31 , 2933.89 ,
2707.67 , 2464.52 , 2199.87 , 1906.67 , 1572.86 , 1174.34 ,
645.351, -261.368])}, {'IPRNod': np.array([4728.5 , 4609.39, 4488.23, 4364.17, 4236.96, 4106.37, 3972.09,
3833.79, 3691.08, 3543.5 , 3390.5 , 3231.42, 3065.47, 2891.66,
2708.71, 2515.01, 2308.36, 2085.72, 1842.6 , 1571.95])}, {'IPRNod': np.array([4647.81, 4532.68, 4417.56, 4302.43, 4187.31, 4072.18, 3957.06,
3841.57, 3723.42, 3601.99, 3476.97, 3348.02, 3214.72, 3076.57,
2933. , 2783.26, 2626.47, 2461.49, 2286.82, 2100.47])}, {'IPRNod': np.array([4539.7 , 4402.95 , 4266.19 , 4129.43 , 3992.67 , 3855.73 ,
3715.47 , 3570.37 , 3419.87 , 3263.34 , 3099.99 , 2928.84 ,
2748.64 , 2557.8 , 2354.16 , 2134.76 , 1895.23 , 1628.76 ,
1323.48 , 954.945])}]}
df = pd.DataFrame(data)
# display(df)
Time Liqwt IPRwt LiqNod IPRNod
0 2018-01-27 {'Liqwt': [18.1123, 969.674, 1921.23, 2872.8, 3824.36, 4775.92, 5727.48, 6679.04, 7630.6, 8582.17, 9533.73, 10485.3, 11436.8, 12388.4, 13340.0, 14291.5, 15243.1, 16194.7, 17146.2, 18097.8]} {'IPRwt': [3919.98, 3782.97, 3641.74, 3496.72, 3345.06, 3185.46, 3019.55, 2846.7, 2667.38, 2482.27, 2289.33, 2087.15, 1874.18, 1647.9, 1403.7, 1127.59, 812.477, 436.561, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4970.64, 4842.67, 4714.7, 4586.73, 4458.76, 4330.79, 4202.82, 4074.85, 3946.88, 3818.9, 3690.93, 3562.96, 3434.99, 3307.02, 3179.05, 3051.08, 2923.11, 2795.14, 2667.17, 2539.2]}
1 2018-05-07 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3823.82, 3708.6, 3578.81, 3441.95, 3299.97, 3153.51, 3002.86, 2848.15, 2689.46, 2526.85, 2360.33, 2190.32, 2017.0, 1839.47, 1656.83, 1469.83, 1276.69, 1073.84, 854.08, 600.952]} {'LiqNod': [38.0128, 2035.08, 4032.16, 6029.23, 8026.3, 10023.4, 12020.4, 14017.5, 16014.6, 18011.7, 20008.7, 22005.8, 24002.9, 25999.9, 27997.0, 29994.1, 31991.2, 33988.2, 35985.3, 37982.4]} {'IPRNod': [4862.36, 4703.68, 4545.01, 4386.33, 4227.48, 4064.74, 3896.13, 3720.94, 3538.32, 3347.22, 3146.31, 2933.89, 2707.67, 2464.52, 2199.87, 1906.67, 1572.86, 1174.34, 645.351, -261.368]}
2 2018-08-15 {'Liqwt': [38.0128, 2035.08, 4032.16, 6029.23, 8026.3, 10023.4, 12020.4, 14017.5, 16014.6, 18011.7, 20008.7, 22005.8, 24002.9, 25999.9, 27997.0, 29994.1, 31991.2, 33988.2, 35985.3, 37982.4]} {'IPRwt': [3748.33, 3613.96, 3462.58, 3303.63, 3138.5, 2962.72, 2777.24, 2580.63, 2372.99, 2153.53, 1920.58, 1668.52, 1387.85, 1056.6, 701.63, 192.702, 0.0, 0.0, 0.0, -1.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4728.5, 4609.39, 4488.23, 4364.17, 4236.96, 4106.37, 3972.09, 3833.79, 3691.08, 3543.5, 3390.5, 3231.42, 3065.47, 2891.66, 2708.71, 2515.01, 2308.36, 2085.72, 1842.6, 1571.95]}
3 2018-11-23 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3722.0, 3637.19, 3522.64, 3392.73, 3250.45, 3097.7, 2935.76, 2763.18, 2580.25, 2386.64, 2179.46, 1955.4, 1709.25, 1430.54, 1112.86, 780.77, 327.827, 0.0, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4647.81, 4532.68, 4417.56, 4302.43, 4187.31, 4072.18, 3957.06, 3841.57, 3723.42, 3601.99, 3476.97, 3348.02, 3214.72, 3076.57, 2933.0, 2783.26, 2626.47, 2461.49, 2286.82, 2100.47]}
4 2019-03-03 {'Liqwt': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRwt': [3581.5, 3486.05, 3372.4, 3250.96, 3123.71, 2991.04, 2852.21, 2706.23, 2552.53, 2389.3, 2216.65, 2033.92, 1839.21, 1629.9, 1400.3, 1138.24, 838.932, 514.519, 0.0, 0.0]} {'LiqNod': [40.0, 2141.47, 4242.95, 6344.42, 8445.89, 10547.4, 12648.8, 14750.3, 16851.8, 18953.3, 21054.7, 23156.2, 25257.7, 27359.2, 29460.6, 31562.1, 33663.6, 35765.0, 37866.5, 39968.0]} {'IPRNod': [4539.7, 4402.95, 4266.19, 4129.43, 3992.67, 3855.73, 3715.47, 3570.37, 3419.87, 3263.34, 3099.99, 2928.84, 2748.64, 2557.8, 2354.16, 2134.76, 1895.23, 1628.76, 1323.48, 954.945]}
- In the case of this data, the
'Time'column isdatetime64[ns] Dtype, and the columns to be plotted, contain rows ofdictwith anp.array, the columns don’t containstring literals.
- Extract all the arrays from the dictionaries
- Use
.applywith.map(dict.values), andpd.Series.explode .apply(lambda x: x.map(dict.values))produces:dict_values([array([ 18.1123, 969.674 , ... , 17146.2 , 18097.8 ])])
- Each
arrayis then extracted fromdict_values(...)with.apply(pd.Series.explode).
- Use
- Create colors for the plots, because the standard color cycler doesn’t have enough unique colors. The easiest way is the use
sns.color_palette.seabornis a high-level API formatplotlib.- See creating over 20 unique legend colors using matplotlib for other alternatives.
- Choosing Colormaps in Matplotlib & Choosing color palettes in seaborn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# load the data from the file, or use the sample df in the OP
df = pd.read_pickle('df3result.pk')
# 1.
df.loc[:, ['Liqwt', 'IPRwt', 'LiqNod', 'IPRNod']] =
df.loc[:, ['Liqwt', 'IPRwt', 'LiqNod', 'IPRNod']].apply(lambda x: x.map(dict.values)).apply(pd.Series.explode)
# 2.
colors = sns.color_palette('husl', n_colors=len(df))
# plot
plt.rcParams.update({'font.size': 16})
fig, ax = plt.subplots(figsize=(12, 9))
# iterate through each row of the dataframe, zipped to the color for that row
for (_, (time, liqwt, iprwt)), color in zip(df[['Time', 'Liqwt', 'IPRwt']].iterrows(), colors):
ax.plot(liqwt, iprwt, '.-', color=color, label=time.date())
ax.set(xlabel='Oil Rate, BOPD', ylabel='$P_{wf},psi$')
ax.legend(bbox_to_anchor=(1, 1.02), loc='upper left', frameon=False)
plt.show()