I want the x values to be "yyyy-mm" from 2016-01 to 2020-01 (interval per half year). However, tried many ways the x values still in a mess.
My code is as follows:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.read_csv('fcc-forum-pageviews.csv',index_col='date')
df = df[(df['value'] >= df['value'].quantile(0.025)) & (df['value'] <= df['value'].quantile(0.975))]
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(9)
ax = fig.add_subplot(1,1,1)
ax.set_title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')
ax.set_xlabel('Date')
ax.set_ylabel('Page Views')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.plot(df['value'],color='r')
The following is the result:
However, what I expected should be:
The dataset can be downloaded from here.Any help is highly appreciated.
After #r-beginners help, I have updated the code as follows:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.read_csv('fcc-forum-pageviews.csv',index_col='date')
df = df[(df['value'] >= df['value'].quantile(0.025)) & (df['value'] <= df['value'].quantile(0.975))]
df.index = pd.to_datetime(df.index)
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(9)
ax = fig.add_subplot(1,1,1)
ax.set_title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')
ax.set_xlabel('Date')
ax.set_ylabel('Page Views')
ax.set_xticks(range(len(df.index)))
ax.set_xticklabels(range(len(df.index)))
months = mdates.MonthLocator(interval=6)
months_fmt = mdates.DateFormatter('%Y-%m')
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(months_fmt)
ax.plot(df['value'], color='r')
now it can display the x values in 6-month interval. However, instead of starting from 2016-07 (the date from the dataframe), it starts from 1970-02. Any hints? Thanks.
Convert the date data to pd.to_datetime()
You can use MonthLocator to control it. The interval is set to 6 months.
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.read_csv('fcc-forum-pageviews.csv',index_col='date')
df = df[(df['value'] >= df['value'].quantile(0.025)) & (df['value'] <= df['value'].quantile(0.975))]
df.index = pd.to_datetime(df.index)
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(9)
ax = fig.add_subplot(1,1,1)
ax.set_title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')
ax.set_xlabel('Date')
ax.set_ylabel('Page Views')
# ax.set_xticks(range(len(df.index)))
# ax.set_xticklabels(range(len(df.index)))
# months = mdates.MonthLocator(interval=6)
# months_fmt = mdates.DateFormatter('%Y-%m')
# ax.xaxis.set_major_locator(months)
# ax.xaxis.set_major_formatter(months_fmt)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.plot(df['value'], color='r')
Please read on Xticks
From the docs
>>> locs, labels = xticks() # Get the current locations and labels.
>>> xticks(np.arange(0, 1, step=0.2)) # Set label locations.
>>> xticks(np.arange(3), ['Tom', 'Dick', 'Sue']) # Set text labels.
>>> xticks([0, 1, 2], ['January', 'February', 'March'],
... rotation=20) # Set text labels and properties.
>>> xticks([]) # Disable xticks.
Related
I have around 4475 rows of csv data like below:
,Time,Values,Size
0,1900-01-01 23:11:30.368,2,
1,1900-01-01 23:11:30.372,2,
2,1900-01-01 23:11:30.372,2,
3,1900-01-01 23:11:30.372,2,
4,1900-01-01 23:11:30.376,2,
5,1900-01-01 23:11:30.380,,
6,1900-01-01 23:11:30.380,,
7,1900-01-01 23:11:30.380,,
8,1900-01-01 23:11:30.380,,321
9,1900-01-01 23:11:30.380,,111
.
.
4474,1900-01-01 23:11:32.588,,
When I try to create simple seaborn lineplot with below code. It creates line chart but its continuous chart while my data i.e. 'Values' has many empty/nan values which should show as gap on chart. How can I do that?
[from datetime import datetime
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("Data.csv")
sns.set(rc={'figure.figsize':(13,4)})
ax =sns.lineplot(x="Time", y="Values", data=df)
ax.set(xlabel='Time', ylabel='Values')
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()]
As reported in this answer:
I've looked at the source code and it looks like lineplot drops nans from the DataFrame before plotting. So unfortunately it's not possible to do it properly.
So, the easiest way to do it is to use matplotlib in place of seaborn.
In the code below I generate a dataframe like your with 20% of missing values in 'Values' column and I use matplotlib to draw a plot:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({'Time': pd.date_range(start = '1900-01-01 23:11:30', end = '1900-01-01 23:11:30.1', freq = 'L')})
df['Values'] = np.random.randint(low = 2, high = 10, size = len(df))
df['Values'] = df['Values'].mask(np.random.random(df['Values'].shape) < 0.2)
fig, ax = plt.subplots(figsize = (13, 4))
ax.plot(df['Time'], df['Values'])
ax.set(xlabel = 'Time', ylabel = 'Values')
plt.xticks(rotation = 90)
plt.tight_layout()
plt.show()
I'm trying to create a bar plot from a DataFrame with Datetime Index.
This is an example working code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set()
index = pd.date_range('2012-01-01', periods=48, freq='M')
data = np.random.randint(100, size = (len(index),1))
df = pd.DataFrame(index=index, data=data, columns=['numbers'])
fig, ax = plt.subplots()
ax.bar(df.index, df['numbers'])
The result is:
As you can see the white bars cannot be distinguished well with respect of the background (why?).
I tried using instead:
df['numbers'].plot(kind='bar')
import matplotlib.ticker as ticker
ticklabels = df.index.strftime('%Y-%m')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
with this result:
But in this way I lose the automatic xticks labels (and grid) 6-months spacing.
Any idea?
You can just change the style:
import matplotlib.pyplot as plt
index = pd.date_range('2012-01-01', periods=48, freq='M')
data = np.random.randint(100, size = (len(index),1))
df = pd.DataFrame(index=index, data=data, columns=['numbers'])
plt.figure(figsize=(12, 5))
plt.style.use('default')
plt.bar(df.index,df['numbers'],color="red")
You do not actually use seaborn. Replace ax.bar(df.index, df['numbers'])
with
sns.barplot(df.index, df['numbers'], ax=ax)
I am very new to pandas, and I have searched many StackOverflow questions similar to this for changing xtick labels yearly, but they all are different did not solve my problem, so I decided to ask my own question.
Here is my question. I have a mock data frame which I want to plot yearly xticks in the x-axis.
import numpy as np
import pandas as pd
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
df.plot()
This gives:
Xticks ==> 1995 2000 2005 etc
But I want ==> 1991 1992 ... 2019
How to do that?
So far I have tried this:
import matplotlib
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
fig,ax = plt.subplots()
df.plot(ax=ax)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
# ax.xaxis.set_minor_locator(matplotlib.dates.YearLocator(base=1))
# ax.set_xticklabels(list(df.index.time))
This gives just 2005 as xtick and nothing has worked till now.
Links I looked:
- Changing xticks in a pandas plot
- Python: Change the time on xticks for Pandas Plot
- https://matplotlib.org/3.1.1/api/dates_api.html
You need to use the x_compat=True argument to have pandas choose the units in a way that they are compatible with matplotlib.dates locators and formatters.
df.plot(ax=ax, x_compat=True)
Complete code:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
fig,ax = plt.subplots()
df.plot(ax=ax, x_compat=True)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
plt.show()
You can try this:
import datetime
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
Complete Example
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import datetime
# data
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
plt.show()
This gives:
I am trying to use matplotlib to graph stock prices against a datetime index.
So I have a graph that looks like this:
And I need it to look like this:
I think it might have something to do with xticks, but I cannot figure out how to make xticks work with a datetime index. Thanks for the help.
tesla['Open'].plot(title='Open Price', label = 'Tesla', figsize = (16, 6))
ford['Open'].plot(label = 'ford')
gm['Open'].plot(label = 'GM')
plt.legend()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
Like this:
import pandas as pd
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y-%m')
ax1.xaxis.set_major_formatter(monthyearFmt)
_ = plt.xticks(rotation=45)
Output:
EDIT:
Let's use set_major_locator with MonthLocator
import pandas as pd
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y-%m')
ax1.xaxis.set_major_formatter(monthyearFmt)
ax1.xaxis.set_major_locator(mdates.MonthLocator([1,7]))
_ = plt.xticks(rotation=45)
Output:
I'm trying to adjust the formatting of the date tick labels of the x-axis so that it only shows the Year and Month values. From what I've found online, I have to use mdates.DateFormatter, but it's not taking effect at all with my current code as is. Anyone see where the issue is? (the dates are the index of the pandas Dataframe)
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
basicDF['some_column'].plot(ax=ax, kind='bar', rot=75)
ax.xaxis_date()
Reproducible scenario code:
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=rng)
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
blah.plot(ax=ax, kind='bar')
ax.xaxis_date()
Still can't get just the year and month to show up.
If I set the format after .plot , get an error like this:
ValueError: DateFormatter found a value of x=0, which is an illegal date. This usually occurs because you have not informed the axis that it is plotting dates, e.g., with ax.xaxis_date().
It's the same for if I put it before ax.xaxis_date() or after.
pandas just doesn't work well with custom date-time formats.
You need to just use raw matplotlib in cases like this.
import numpy
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas
N = 20
numpy.random.seed(N)
dates = pandas.date_range('1/1/2014', periods=N, freq='m')
df = pandas.DataFrame(
data=numpy.random.randn(N),
index=dates,
columns=['A']
)
fig, ax = plt.subplots(figsize=(10, 6))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.bar(df.index, df['A'], width=25, align='center')
And that gives me:
Solution with pandas only
You can create nicely formatted ticks by using the DatetimeIndex and taking advantage of the datetime properties of the timestamps. Tick locators and formatters from matplotlib.dates are not necessary for a case like this unless you would want dynamic ticks when using the interactive interface of matplotlib for zooming in and out (more relevant for time ranges longer than in this example).
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
# Create sample time series with month start frequency, plot it with a pandas bar chart
rng = np.random.default_rng(seed=1) # random number generator
dti = pd.date_range('1/1/2014', periods=20, freq='m')
df = pd.DataFrame(data=rng.normal(size=dti.size), index=dti)
ax = df.plot.bar(figsize=(10,4), legend=None)
# Set major ticks and tick labels
ax.set_xticks(range(df.index.size))
ax.set_xticklabels([ts.strftime('%b\n%Y') if ts.year != df.index[idx-1].year
else ts.strftime('%b') for idx, ts in enumerate(df.index)])
ax.figure.autofmt_xdate(rotation=0, ha='center');
The accepted answer claims that "pandas won't work well with custom date-time formats", but you can make use of pandas' to_datetime() function to use your existing datetime Series in the dataframe:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=pd.to_datetime(rng))
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(DateFormatter('%m-%Y'))
ax.bar(blah.index, blah[0], width=25, align='center')
Will result in:
You can see the different available formats here.
I stepped into the same problem and I used an workaround to transform the index from date time format into the desired string format:
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=rng)
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
# transform index to strings
blah_test = blah.copy()
str_index = []
for s_year,s_month in zip(blah.index.year.values,blah.index.month.values):
# build string accorind to format "%Y-%m"
string_day = '{}-{:02d}'.format(s_year,s_month)
str_index.append(string_day)
blah_test.index = str_index
blah_test.plot(ax=ax, kind='bar', rot=45)
plt.show()
which results in the following figure: