How to plot dates on a monthly/weekly basis in pandas dataframe
only plot months or weeks
Use DataFrame.resample
or Series.resample
to resample the frequency to M
(months) or W
(weeks). Just make sure the index is a true datetime
type (use pd.to_datetime
if needed).
Before resampling:
rng = np.random.default_rng(0)
df = pd.DataFrame({'Date': pd.date_range('2021-01-01', '2021-12-31'), 'Partei': rng.choice(list('abc'), size=365), 'sentiment_prediction': rng.integers(5, size=365)})
df = df.set_index('Date')
df['sentiment_prediction'].plot()
After resampling:
# resampled weekly
df['sentiment_prediction'].resample('W').mean().plot()
# resampled monthly
df['sentiment_prediction'].resample('M').mean().plot()
grouped by "Partei"
Use DataFrame.pivot_table
to pivot Partei
into columns before plotting:
(df.pivot_table(index='Date', columns='Partei', values='sentiment_prediction')
.resample('M').mean().plot())
How to plot pandas DataFrame with date (Year/Month)?
Using pivot
and plot
(A
isn't showing up because it only has a single point and is getting hidden by the first point of M
). You also have to convert your Date
column to datetime
in order to accurately display the X-Axis:
df.Date = pd.to_datetime(df.Date)
df.pivot(index='Date', columns='val', values='count').plot(marker='o')
If you'd like to show NaN
values as zero instead, just use fillna
:
df.pivot(index='Date', columns='val', values='count').fillna(0).plot(marker='o')
Month,Year with Value Plot,Pandas and MatPlotLib
One way is this:
import pandas as pd
import matplotlib.pyplot as plt
df['date'] = df['month'].map(str)+ '-' +df['year'].map(str)
df['date'] = pd.to_datetime(df['date'], format='%m-%Y').dt.strftime('%m-%Y')
fig, ax = plt.subplots()
plt.plot_date(df['date'], df['Value'])
plt.show()
How can I plot only the month and the day (without the year) from this Pandas data-series?
You can change the date index format of spare
with pandas.DatetimeIndex.strftime()
spare.index = spare.index.strftime('%m-%d')
Or you can convert Series spare to Dataframe and reset Date index to column, then change Date column format with pandas.Series.dt.strftime().
spare = spare.to_frame().reset_index()
spare['Date'] = spare['Date'].dt.strftime('%m-%d')
plt.plot(spare['Date'], (spare[[0]] - 1) * 100)
Sort df by date column by month
df = df.groupby(df['Date'].dt.strftime('%Y-%m')).max()
df["Month"] = pd.to_datetime(df.Date, format='%b', errors='coerce').dt.month
df = df.sort_values(by="Month").drop(['Month'], axis=1)
Example of the code result:
- start df:
- result df:
Python pandas scatterplot of year against month-day
Some Sample Data:
import pandas as pd
from matplotlib import pyplot as plt, dates as mdates
# Some Sample Data
df = pd.DataFrame({
'date': pd.date_range(
start='2000-01-01', end='2020-12-31', freq='D'
)
}).sample(n=100, random_state=5).sort_values('date').reset_index(drop=True)
Then one option would be to normalize the dates to the same year. Any year works as long as it's a leap year to handle the possibility of a February 29th (leap day).
This becomes the new y-axis.
# Create New Column with all dates normalized to same year
# Any year works as long as it's a leap year in case of a Feb-29
df['month-day'] = pd.to_datetime('2000-' + df['date'].dt.strftime('%m-%d'))
# Plot DataFrame
ax = df.plot(kind='scatter', x='date', y='month-day')
# Set Date Format On Axes
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) # Year Only
ax.yaxis.set_major_formatter(mdates.DateFormatter('%m-%d')) # No Year
plt.tight_layout()
plt.show()
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