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How to plot all the xaxis values in a df.plot?

I have this df:
CODE MONTH PP
24 000136 Enero 57.9
25 000136 Febrero 124.3
26 000136 Marzo 147.7
27 000136 Abril 71.5
28 000136 Mayo 13.2
... ... ...
I'm plotting the figure with this code:
fig = plt.figure('Graphic', figsize=(30,15), dpi=150)
ax1 = fig.add_axes([0.2, 0.25, 0.60, 0.60])
df.plot(x='MONTH',y='PP',style='--o',color='black',linewidth=8,marker='o',markersize=12, ax=ax1)
ax1.xaxis.set_major_locator(MaxNLocator(min_n_ticks=12))
I want to see all the 12 values of the months in my xaxis figure but i only got 6 of them in the figure.
I tried with ax1.xaxis.set_major_locator(MaxNLocator(min_n_ticks=12)) but it only plot the ticks, not the values in the xaxis.
Do you know how can i plot all the xaxis values?
I can only use a df.plot
Thanks in advance.

The x-axis scale is automatically displayed, so add the number of pieces you wish to display to the x-axis setting. This will display 12 months. The missing data is added as appropriate.
import matplotlib.pyplot as plt
fig = plt.figure('Graphic', figsize=(20,10), dpi=150)
ax1 = fig.add_axes([0.2, 0.25, 0.60, 0.60])
df.plot(x='MONTH',y='PP',style='--o',color='black',linewidth=8,markersize=12, xticks=range(12), ax=ax1)
plt.show()

plot and draw curves in python matplotlib without ignoring first and last Nan values from the graph figure

I have csv format file like the below table
depth
x1
x2
x3
1000
15
Nan
Nan
1001
10
Nan
Nan
1002
5
Nan
Nan
1003
8
10
Nan
1004
12
11.11111111
Nan
1010
13
17.77777778
14.16666667
1011
14
18.88888889
15
1012
15
20
15.71428571
1013
16
20.55555556
16.42857143
1014
17
21.11111111
17.14285714
1017
20
22.77777778
19.28571429
1018
21
23.33333333
20
1019
22
23.88888889
20.83333333
1024
27
17.5
25
1025
28
15
25
1026
25
Nan
Nan
1027
26
Nan
Nan
1028
7
Nan
Nan
I want to plot x1, x2, x3 columns versus depth columns but sometimes these columns contain Nan values at start and end of columns, I want to plot whole curves points without ignoring the first and last Nan values
the below code is my attempt to plot curves but the plot always start and end at first and last valid values and ignores the first and last Nan values
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
df = pd.read_csv("result.csv")
fig = plt.figure(figsize=(15, 12), dpi=100, tight_layout=True)
gs = gridspec.GridSpec(nrows=1, ncols=5, wspace=0)
fig.add_subplot(gs[0, 1])
plt.plot(df['x1'],df["depth"], linewidth=2, color='black', marker="o", markersize=3)
plt.gca().invert_yaxis()
fig.add_subplot(gs[0,2 ])
plt.plot(df["x2"],df["depth"], linewidth =2, color='black', marker="o", markersize=3)
plt.gca().invert_yaxis()
fig.add_subplot(gs[0,3])
plt.plot(df["x3"],df["depth"], linewidth =2, color='black', marker="o", markersize=3)
plt.gca().invert_yaxis()
plt.show()
the current reult
the desired result in the below image where all curves y axis start from same depth point

You need to share the y axis with the other y axis:
fig, axs = plt.subplots(1, 3, figsize=(15, 12), dpi=100, tight_layout=True, gridspec_kw={'wspace': 0})
axs[0].plot(df.x1, df.depth, '-ok', lw=2, ms=3)
axs[1].plot(df.x2, df.depth, '-ok', lw=2, ms=3)
axs[1].sharey(axs[0])
axs[2].plot(df.x3, df.depth, '-ok', lw=2, ms=3)
axs[2].sharey(axs[0])

[Python3]How to use Seaborn/Matplotlib to graph pandas dataframe

I'm still having troubles to do this
Here is how my data looks like:
date positive negative neutral
0 2015-09 23 6 18
1 2016-04 709 288 704
2 2016-08 1478 692 1750
3 2016-09 1881 926 2234
4 2016-10 3196 1594 3956
in my csv file I don't have those 0-4 indexes, but only 4 columns from 'date' to 'neutral'.
I don't know how to fix my codes to get it look like this
Seaborn code
sns.set(style='darkgrid', context='talk', palette='Dark2')
fig, ax = plt.subplots(figsize=(8, 8))
sns.barplot(x=df['positive'], y=df['negative'], ax=ax)
ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])
ax.set_ylabel("Percentage")
plt.show()

To do this in seaborn you'll need to transform your data into long format. You can easily do this via melt:
plotting_df = df.melt(id_vars="date", var_name="sign", value_name="percentage")
print(plotting_df.head())
date sign percentage
0 2015-09 positive 23
1 2016-04 positive 709
2 2016-08 positive 1478
3 2016-09 positive 1881
4 2016-10 positive 3196
Then you can plot this long-format dataframe with seaborn in a straightforward mannter:
sns.set(style='darkgrid', context='talk', palette='Dark2')
fig, ax = plt.subplots(figsize=(8, 8))
sns.barplot(x="date", y="percentage", ax=ax, hue="sign", data=plotting_df)

Based on the data you posted
sns.set(style='darkgrid', context='talk', palette='Dark2')
# fig, ax = plt.subplots(figsize=(8, 8))
df.plot(x="date",y=["positive","neutral","negative"],kind="bar")
plt.xticks(rotation=-360)
# ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])
# ax.set_ylabel("Percentage")
plt.show()

plot from pandas dataframe with negative and positive values

I have a dataframe which looks like this:
MM Initial Energy MM Initial Angle QM Energy QM Angle
0 13.029277 120.0 18.048 120.0
1 11.173115 125.0 15.250 125.0
2 9.411475 130.0 12.668 130.0
3 7.762888 135.0 10.309 135.0
4 6.239025 140.0 8.180 140.0
5 4.853004 145.0 6.286 145.0
6 3.617394 150.0 4.633 150.0
7 2.544760 155.0 3.226 155.0
8 1.646335 160.0 2.070 160.0
9 0.934298 165.0 1.166 165.0
10 0.419003 170.0 0.519 170.0
11 0.105913 175.0 0.130 175.0
12 0.000000 -180.0 0.000 -180.0
13 0.105988 -175.0 0.130 -175.0
14 0.420029 -170.0 0.519 -170.0
15 0.937312 -165.0 1.166 -165.0
16 1.650080 -160.0 2.070 -160.0
17 2.548463 -155.0 3.227 -155.0
18 3.621227 -150.0 4.633 -150.0
19 4.856266 -145.0 6.286 -145.0
20 6.236939 -140.0 8.180 -140.0
21 7.760035 -135.0 10.309 -135.0
22 9.409117 -130.0 12.669 -130.0
23 11.170671 -125.0 15.251 -125.0
24 13.033293 -120.0 18.048 -120.0
I want to plot the data with Angles on the x-axis and energy on the y. This sounds fairly simple, however what happens is that pandas or matplotlib sorts the X-axis values in a such a manner that my plot looks split. This is what it looks like:
However, this is how I want it:
My code is as follows:
df=pd.read_fwf('scan_c1c2c3h31_orig.txt', header=None, prefix='X')
df.rename(columns={'X0':'MM Initial Energy',
'X1':'MM Initial Angle',
'X2':'QM Energy', 'X3':'QM Angle'},
inplace=True)
df=df.sort_values(by=['MM Initial Angle'], axis=0, ascending=True)
df=df.reset_index(drop=False)
df2=pd.read_fwf('scan_c1c2c3h31.txt', header=None, prefix='X')
df2.rename(columns={'X0':'MM Energy',
'X1':'MM Angle',
'X2':'QM Energy', 'X3':'QM Angle'},
inplace=True)
df2=df2.sort_values(by=['MM Angle'], axis=0, ascending=True)
df2=df2.reset_index(drop=False)
df
df2
ax = plt.axes()
df.plot(y="MM Initial Energy", x="MM Initial Angle", color='red', linestyle='dashed',linewidth=2.0, ax=ax, fontsize=20, legend=True)
df2.plot(y="MM Energy", x="MM Angle", color='red', ax=ax, linewidth=2.0, fontsize=20, legend=True)
df2.plot(y="QM Energy", x="QM Angle", color='blue', ax=ax, linewidth=2.0, fontsize=20, legend=True)
plt.ylim(-0.05, 6)
ax.xaxis.set_major_locator(MultipleLocator(20))
ax.xaxis.set_minor_locator(MultipleLocator(10))
ax.yaxis.set_minor_locator(MultipleLocator(0.5))
plt.xlabel('Angles (Degrees)', fontsize=25)
plt.ylabel('Energy (kcal/mol)', fontsize=25)
What I am doing is, sorting the dataframe by 'MM Angles'/'MM Initial Angles' to avoid plot "scarambling" due to repeating values in the y-axis.The angles vary from -180 to 180, where I want the -180 and +180 next to each other.
I have tried sorting the negative values in ascending order and positive values in descending order as suggested in this post, but I still get the same plot where x axis ranges from -180 to +180.
I have also tried matplotlib axis spines to recenter the plot, and I have also tried inverting the x-axis as suggested in this post, but still get the same plot. Additionally, I have also tried suggestion in this another post.
Any help will be appreciated.

If you don't need to rescale the plot, I would plot against the positive angles 0-360 and manually re-label the ticks:
fig, ax = plt.subplots()
(df.assign(Angle=df['MM Initial Angle']%360)
.plot(x='Angle', y=['QM Energy','MM Initial Energy'], ax=ax)
)
ax.xaxis.set_major_locator(MultipleLocator(20))
x_ticks = ax.get_xticks()
x_ticks = [t-360 if t>180 else t for t in x_ticks]
ax.set_xticklabels(x_ticks)
plt.plot()
Output:

How to make plots with small whitespace separations in Matplotlib or Seaborn?

I'd like to make this type of plot with multiple columns separated by small whitespace, each having different category having 3-5 (5 in this example) different observations with varying values on y axis:

actually, i can plot this plot use ggplot2. for example:
head(mtcars)
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
library(dplyr)
library(ggplot2)
mtcars %>% reshape2::melt() %>%
ggplot(aes(x = variable, y = value)) +
geom_point() + facet_grid(~ variable) +
theme(axis.text.x = element_blank())
you set a categorical variable in your dataset,then use the facet_grid(~).this function can change your plot into multiple plot by your categrical variable

Here is an approach to draw a similar plot using Python's matplotlib. The plot has a grey background and white major and minor gridlines to delimit the zones. Getting the dots in the center of each little cell is somewhat tricky: divide into n+1 spaces and shift half a cell (1/2n). A secondary x-axis can be used to set the labels. A zorder has to be set to have the dots on top of the gridlines.
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import ticker
n = 5
cols = 7
values = [np.random.uniform(1, 10, n) for c in range(cols)]
fig, ax = plt.subplots()
ax.set_facecolor('lightgrey')
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.xaxis.set_minor_locator(ticker.MultipleLocator(1 / (n)))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
ax.grid(True, which='both', axis='both', color='white')
ax.set_xticklabels([])
ax.tick_params(axis='x', which='both', length=0)
ax.grid(which='major', axis='both', lw=3)
ax.set_xlim(1, cols + 1)
for i in range(1, cols + 1):
ax.scatter(np.linspace(i, i + 1, n, endpoint=False) + 1 / (2 * n), values[i-1], c='crimson', zorder=2)
ax2 = ax.twiny()
ax2.set_xlim(0.5, cols + 0.5)
ticks = range(1, cols + 1)
ax2.set_xticks(ticks)
ax2.set_xticklabels([f'Cat_{t:02d}' for t in ticks])
bbox = dict(boxstyle="round", ec="limegreen", fc="limegreen", alpha=0.5)
plt.setp(ax2.get_xticklabels(), bbox=bbox)
ax2.tick_params(axis='x', length=0)
plt.show()