How to Plot Multiple Pandas Columns

matplotlib: plot multiple columns of pandas data frame on the bar chart

You can plot several columns at once by supplying a list of column names to the plot's y argument.

df.plot(x="X", y=["A", "B", "C"], kind="bar")

This will produce a graph where bars are sitting next to each other.

In order to have them overlapping, you would need to call plot several times, and supplying the axes to plot to as an argument ax to the plot.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

y = np.random.rand(10,4)
y[:,0]= np.arange(10)
df = pd.DataFrame(y, columns=["X", "A", "B", "C"])

ax = df.plot(x="X", y="A", kind="bar")
df.plot(x="X", y="B", kind="bar", ax=ax, color="C2")
df.plot(x="X", y="C", kind="bar", ax=ax, color="C3")

plt.show()

How to plot multiple pandas columns

Pandas.DataFrame.plot() per default uses index for plotting X axis, all other numeric columns will be used as Y values.

So setting year column as index will do the trick:

total_year.set_index('year').plot(figsize=(10,5), grid=True)

Plotting multiple columns on the same figure using pandas

Your data.plot.line returns an AxesSubplot instance, you can catch it and feed it to your second command:

# plot 1
ax = data.plot.line(…)

# plot 2
data.plot.line(…, ax=ax)

Try to run this code:

# convert data to float format
data = data.astype(float)
orig_data = orig_data.astype(float)

# Plot all columns of synthetic data in grey
ax = data.plot.line(title="ARMA Synthetic Temperature Histories",
                    xlabel="Time (yrs)",
                    ylabel=("Synthetic avergage hourly temperature (C)"),
                    color="#929591",
                    legend=None)

# Plot one column of original data in black


orig_data.plot.line(color="k",legend="Original temperature data", ax=ax)

# Create and save figure
ax.figure.savefig("temp_arma.png")

How to plot multiple dataframe columns with options for marker, color, and lw

• Plot directly with pandas.DataFrame.plot
  
  • This allows for multiple color, but not multiple marker, or linewidth, however, style will accept a list with a combination of marker, linestyle, and color. See the Notes section of matplotlib.pyplot.plot for the available fmt options for style.
• Using the data from the OP, in a dataframe (df).
• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3

colors = ['#89FAB4', '#FAE4A0', '#FA837D', '#B049E3', '#E3BA5F', '#E35E54', '#6591EA', '#EB83C6', '#EB1551', '#1802F4']
styles = ['', '', '-+', '-o', '', '', '', '', '', '']

ax = df.plot(x='Year', y=df.columns[2:], style=styles, color=colors, figsize=(16, 9))  # plot the dataframe and set Time as x
fig = ax.get_figure()  # extract the figure object
ax.set_xticks(df.Year)  # set the xticks
ax.legend(bbox_to_anchor=(1, 1.01), loc='upper left') # move the legend
fig.tight_layout(pad=3)
fig.suptitle('Power Generated by Renewable Energy sources (TWh)', fontsize=22, y=1.02, color='#A2BDF2')

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• Alternatively, combine the values for each plot using zip, and iterate through each combination of values.

markers = ['', '', '+', 'o', '', '', '', '', '', '']
colors = ['#89FAB4', '#FAE4A0', '#FA837D', '#B049E3', '#E3BA5F', '#E35E54', '#6591EA', '#EB83C6', '#EB1551', '#1802F4']
lws = [3, 2, 1, 2, 2, 2, 1, 1, 1, 1]
columns = df.columns[2:]  # select all the columns except Year and World

fig, ax = plt.subplots(figsize=(16, 9)) 
for marker, color, lw, col in zip(markers, colors, lws, columns):
    df.plot(x='Year', y=col, marker=marker, color=color, lw=lw, label=col, ax=ax)
    
ax.set_xticks(df.Year)
ax.legend(bbox_to_anchor=(1, 1.01), loc='upper left')
fig.tight_layout(pad=3)
fig.suptitle('Power Generated by Renewable Energy sources (TWh)', fontsize=22, y=1.02, color='#A2BDF2')
plt.show()

How to plot a pandas DataFrame with multiple axes each rendering multiple columns?

I assume you are working with a dataframe like this:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

df = pd.DataFrame({'time': pd.date_range(start = '2020-01-01', end = '2020-01-10', freq = 'D')})
df['price1'] = np.random.random(len(df))
df['price2'] = np.random.random(len(df))
df['returns'] = np.random.random(len(df))
df['growth'] = np.random.random(len(df))

        time    price1    price2   returns    growth
0 2020-01-01  0.374540  0.020584  0.611853  0.607545
1 2020-01-02  0.950714  0.969910  0.139494  0.170524
2 2020-01-03  0.731994  0.832443  0.292145  0.065052
3 2020-01-04  0.598658  0.212339  0.366362  0.948886
4 2020-01-05  0.156019  0.181825  0.456070  0.965632
5 2020-01-06  0.155995  0.183405  0.785176  0.808397
6 2020-01-07  0.058084  0.304242  0.199674  0.304614
7 2020-01-08  0.866176  0.524756  0.514234  0.097672
8 2020-01-09  0.601115  0.431945  0.592415  0.684233
9 2020-01-10  0.708073  0.291229  0.046450  0.440152

Then a possible function could be:

def plot_df(df, x_column, columns):

    cmap = cm.get_cmap('tab10', 10)

    fig, ax = plt.subplots()

    axes = [ax]
    handles = []

    for i, _ in enumerate(range(len(columns) - 1)):
        twin = ax.twinx()
        axes.append(twin)
        twin.spines.right.set_position(("axes", 1 + i/10))

    j = 0
    for i, col in enumerate(columns):
        ylabel = []
        if len(col) == 1:
            p, = axes[i].plot(df[x_column], df[col[0]], label = col[0], color = cmap(j)[:3])
            ylabel.append(col[0])
            handles.append(p)
            j += 1
        else:

            for sub_col in col:
                p, = axes[i].plot(df[x_column], df[sub_col], label = sub_col, color = cmap(j)[:3])
                ylabel.append(sub_col)
                handles.append(p)
                j += 1
        axes[i].set_ylabel(', '.join(ylabel))

    ax.legend(handles = handles, frameon = True)

    plt.tight_layout()

    plt.show()

If you call the above function with:

plot_df(df, 'time', [['price1', 'price2'], ['returns'], ['growth']])

then you will get:

NOTES

The first element of columns list (['price1', 'price2'] in this case) is always drawn on the left axis, other elements on the right ones.

Python - Plot multiple dataframe columns

I'd recommend seaborn for this kind of plots:

import seaborn as sns
sns.barplot(df.Cont+'\n'+df.Coun, 'Y1', hue='X3', data=df)

---

For adjusting figure size you can create a figure with a subplot first and then put the seaborn plot into the desired destination with the ax kwarg:

import seaborn as sns
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(16, 8))
sns.barplot(df.Cont+'\n'+df.Coun, 'Y1', hue='X3', data=df, ax=ax)

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