Matplotlib Bar Chart with Dates

matplotlib bar chart with dates

All plot_date does is plot the function and the call ax.xaxis_date().

All you should need to do is this:

import numpy as np
import matplotlib.pyplot as plt
import datetime

x = [datetime.datetime(2010, 12, 1, 10, 0),
    datetime.datetime(2011, 1, 4, 9, 0),
    datetime.datetime(2011, 5, 5, 9, 0)]
y = [4, 9, 2]

ax = plt.subplot(111)
ax.bar(x, y, width=10)
ax.xaxis_date()

plt.show()

Matplotlib bar chart on datetime index values

Maybe you are trying to plot too many bars on a small plot. Try fig = plt.figure(figsize=(12,6) to have a bigger plot. You can also pass width=0.9 to your bar command:

fig, ax = plt.subplots(figsize=(12,6))
df.plot.bar(y='Net sales', width=0.9, ax=ax)  # modify width to your liking

Output:

Datetime Stacked Bar Chart with Matplotlib from dataframe

You can use crosstab then plot.bar:

# convert to date, no need for set_index
df.date = pd.to_datetime(df.date)

pd.crosstab(df.date,df.operation).plot.bar(stacked=True)

Output:

If you want monthly, you can use dt.to_period('M') to get monthly dates and crosstab:

pd.crosstab(df.date.dt.to_period('M'),df.operation).plot.bar(stacked=True)

Output (for the sample data):

---

Update to have all the dates on the x-axis:

df['date'] = pd.to_datetime(df.date)

mindate, maxdate = df['date'].agg(['min','max'])

all_dates = pd.date_range(mindate, maxdate, freq='D').date

(pd.crosstab(df.date,df.operation)
   .reindex(all_dates)
   .plot.bar(stacked=True)
)

Output:

Matplotlib - Horizontal Bar Chart Timeline With Dates - Xticks not showing date

You can plot each bar as line, choosing the width of the line (lw) you prefer:

# Set the color of the grid lines
mpl.rcParams['grid.color'] = "w"

fig, ax = plt.subplots(1, 1)
# Plot eac item as a line
for i, (b, e, l) in enumerate(zip(beg_sort, end_sort, evt_sort)):
    ax.plot_date([b, e], [i + 1] * 2, ls='-', marker=None, lw=10)  # 10 for the line width

# Set ticks and labels on y axis
ax.set_yticks(range(1, len(evt_sort) + 1))
ax.set_yticklabels(evt_sort)

# Set color and transparency of the grid
ax.patch.set_facecolor('gray')
ax.patch.set_alpha(0.3)
# activate grid
ax.grid(True)

Moreover, you can play with the background grid, customizing it according to your needs.

Grouped Bar-Chart with customized DateTime Index using pandas and Matplotlib

The reason ax.xaxis.set_major_locator(mdates.MonthLocator()) fails because under the hood, pandas plots the bars against range(len(df)), then rename the ticks accordingly.

You can grab the xticklabels after you plot, and reformat it:

ax = testdata.plot.bar()

ticks = [tick.get_text() for tick in ax.get_xticklabels()]
ticks = pd.to_datetime(ticks).strftime('%b %Y')
ax.set_xticklabels(ticks)

which gives the same result as ImpotanceOfBeingErnest's:

Another, probably better way is to shift the bars of each columns. This works better when you have many columns and want to reduce the number of xticks.

fig, ax = plt.subplots()

# define the shift
shift = pd.to_timedelta('1D')

# modify the base of each columns, can do with a for loop
ax.bar(testdata.index + shift, testdata["A"]) 
ax.bar(testdata.index, testdata["B"])
ax.bar(testdata.index - shift, testdata["C"])
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
plt.show()

Output:

How to Bar Chart (with Dates on x-axis) after Groupby operation

Set date as index. By default, index is plotted on the x axis.

df3.set_index('date').plot.bar()

df3.set_index('date').plot()

Following your comments. Please try

df3.groupby(['date','topic_nmf'])['count'].sum().unstack().plot.bar()
#df3.groupby(['date','topic_nmf'])['count'].sum().unstack().plot()#lineplot

FREQUENCY BAR CHART OF A DATE COLUMN IN AN ASCENDING ORDER OF DATES

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('dataset.csv')
data['sample_date'] = pd.to_datetime(data['sample_date'])
data['sample_date'].value_counts().sort_index().plot(kind='bar') # Use sort_index()

plt.tight_layout()
plt.show()

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