I'm trying to plot a graph of a time series which has dates from 1959 to 2019 including months, and I when I try plotting this time series I'm getting a clustered x-axis where the dates are not showing properly. How is it possible to remove the months and get only the years on the x-axis so it wont be as clustered and it would show the years properly?
fig,ax = plt.subplots(2,1)
ax[0].hist(pca_function(sd_Data))
ax[0].set_ylabel ('Frequency')
ax[1].plot(pca_function(sd_Data))
ax[1].set_xlabel ('Years')
fig.suptitle('Histogram and Time series of Plot Factor')
plt.tight_layout()
# fig.savefig('factor1959.pdf')
pca_function(sd_Data)
comp_0
sasdate
1959-01 -0.418150
1959-02 1.341654
1959-03 1.684372
1959-04 1.981473
1959-05 1.242232
...
2019-08 -0.075270
2019-09 -0.402110
2019-10 -0.609002
2019-11 0.320586
2019-12 -0.303515
[732 rows x 1 columns]
From what I see, you do have years on your second subplot, they are just overlapped because there are to many of them placed horizontally. Try to increase figsize, and rotate ticks:
# Builds an example dataframe.
df = pd.DataFrame(columns=['Years', 'Frequency'])
df['Years'] = pd.date_range(start='1/1/1959', end='1/1/2023', freq='M')
df['Frequency'] = np.random.normal(0, 1, size=(df.shape[0]))
fig, ax = plt.subplots(2,1, figsize=(20, 5))
ax[0].hist(df.Frequency)
ax[0].set_ylabel ('Frequency')
ax[1].plot(df.Years, df.Frequency)
ax[1].set_xlabel('Years')
for tick in ax[0].get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
for tick in ax[1].get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
fig.suptitle('Histogram and Time series of Plot Factor')
plt.tight_layout()
p.s. if the x-labels still overlap, try to increase your step size.
First off, you need to store the result of the call to pca_function into a variable. E.g. called result_pca_func. That way, the calculations (and possibly side effects or different randomization) are only done once.
Second, the dates should be converted to a datetime format. For example using pd.to_datetime(). That way, matplotlib can automatically put year ticks as appropriate.
Here is an example, starting from a dummy test dataframe:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame({'Date': [f'{y}-{m:02d}' for y in range(1959, 2019) for m in range(1, 13)]})
df['Values'] = np.random.randn(len(df)).cumsum()
df = df.set_index('Date')
result_pca_func = df
result_pca_func.index = pd.to_datetime(result_pca_func.index)
fig, ax2 = plt.subplots(figsize=(10, 3))
ax2.plot(result_pca_func)
plt.tight_layout()
plt.show()
Related
I have created a barplot for given days of the year and the number of people born on this given day (figure a). I want to set the x-axes in my seaborn barplot to xlim = (0,365) to show the whole year.
But, once I use ax.set_xlim(0,365) the bar plot is simply moved to the left (figure b).
This is the code:
#data
df = pd.DataFrame()
df['day'] = np.arange(41,200)
df['born'] = np.random.randn(159)*100
#plot
f, axes = plt.subplots(4, 4, figsize = (12,12))
ax = sns.barplot(df.day, df.born, data = df, hue = df.time, ax = axes[0,0], color = 'skyblue')
ax.get_xaxis().set_label_text('')
ax.set_xticklabels('')
ax.set_yscale('log')
ax.set_ylim(0,10e3)
ax.set_xlim(0,366)
ax.set_title('SE Africa')
How can I set the x-axes limits to day 0 and 365 without the bars being shifted to the left?
IIUC, the expected output given the nature of data is difficult to obtain straightforwardly, because, as per the documentation of seaborn.barplot:
This function always treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis, even when the data has a numeric or date type.
This means the function seaborn.barplot creates categories based on the data in x (here, df.day) and they are linked to integers, starting from 0.
Therefore, it means even if we have data from day 41 onwards, seaborn is going to refer the starting category with x = 0, making for us difficult to tweak the lower limit of x-axis post function call.
The following code and corresponding plot clarifies what I explained above:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# data
rng = np.random.default_rng(101)
day = np.arange(41,200)
born = rng.integers(low=0, high=10e4, size=200-41)
df = pd.DataFrame({"day":day, "born":born})
# plot
f, ax = plt.subplots(figsize=(4, 4))
sns.barplot(data=df, x='day', y='born', ax=ax, color='b')
ax.set_xlim(0,365)
ax.set_xticks(ticks=np.arange(0, 365, 30), labels=np.arange(0, 365, 30))
ax.set_yscale('log')
ax.set_title('SE Africa')
plt.tight_layout()
plt.show()
I suggest using matplotlib.axes.Axes.bar to overcome this issue, although handling colors of the bars would be not straightforward compared to sns.barplot(..., hue=..., ...) :
# plot
f, ax = plt.subplots(figsize=(4, 4))
ax.bar(x=df.day, height=df.born) # instead of sns.barplot
ax.get_xaxis().set_label_text('')
ax.set_xlim(0,365)
ax.set_yscale('log')
ax.set_title('SE Africa')
plt.tight_layout()
plt.show()
I'm trying to plot a bar graph that is accompanied by two line graphs. The barplot shows fine but I can't seem to get the lines plotted above the barplot. Here's the code:
fig, ax = plt.subplots(figsize=(18,9))
sns.set_style("darkgrid")
g=sns.barplot(date_new, df["Net xG (xG - Opponent's xG)"].astype("float"), palette="coolwarm_r", hue=df["Net xG (xG - Opponent's xG)"].replace({"-":0}).astype("float"), dodge=False, data=df)
plt.plot(date_new, -df["Opponent's xG"].astype("float"), color="gold", marker="o")
plt.plot(date_new, df["xG (Expected goals)"].astype("float"), color="indianred", marker="o")
g.set_xticklabels(stuff[::-1], rotation=90)
g.get_legend().remove()
g.set(xlim=([-0.8, 46]))
plt.show()
date_new variable used for the x-axis is in datetime64[ns] format. A weird thing I noticed is that if I reformat date_new as a string like date_new.astype("str"), the line plots show but the order is reversed.
I tried to "re-reverse" the order of which dates are sorted by by changing the x-axis variable to date_new[::-1], but that doesn't seem to change the line plots' order.
Here's a screenshot of how the x (Date) and y (xG) axis variables look on the dataframe:
You are trying to combine a bar graph with two line plots. It seems you are having issues matching your x-axis variables. As #Henry Ecker said above, the x axis labels on a bar plot are cosmetic and do not represent an actual date time axis. Consequently, the x-axis values for your bar plot are simply the numbers 0 to 46.
To fix your problem, simply make the line plot x values a list from 0 to 46.
I simulated your data and demonstrate the solution in the example below.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
# create data
# there are 46 rows each representing a game against some other club
# colums include: date of game, opposing club, club goals, opposing club goals
# goal range is 0-5
df = pd.DataFrame({
'date':pd.date_range(start='1/2021', end='7/1/2021', periods=46),
'club':['Team: ' + str(n) for n in range(1,47)],
'goals': np.random.randint(0, 5, 46),
'opposing_goals':np.random.randint(0, 5, 46)
})
df['net_goals'] = df.goals - df.opposing_goals
fig, ax = plt.subplots(figsize=(18,9))
sns.set_style("darkgrid")
g=sns.barplot(
x=df.date, y=df.net_goals,
palette="coolwarm_r", hue=df.net_goals, dodge=False, data=df
)
plt.plot(np.arange(0,46), -df.opposing_goals, color="gold", marker="o")
plt.plot(np.arange(0,46), df.goals, color="indianred", marker="o")
g.set_xticklabels(df.club, rotation=45)
g.get_legend().remove()
g.set(xlim=([-0.8, 46]))
I am trying to plot a simple pandas Series object, its something like this:
2018-01-01 10
2018-01-02 90
2018-01-03 79
...
2020-01-01 9
2020-01-02 72
2020-01-03 65
It includes only the first month of each year, so it only contains the month January and all its values through the days.
When i try to plot it
# suppose the name of the series is dates_and_values
dates_and_values.plot()
It returns a plot like this (made using my current data)
It is clearly plotting by year and then the month, so it looks pretty squished and small, since i don't have any other months except January, is there a way to plot it by the year and day so it outputs a better plot to observe the days.
the x-axis is the index of the dataframe
dates are a continuous series, x-axis is continuous
change index to be a string of values, means it it no longer continuous and squishes your graph
have generated some sample data that only has January to demonstrate
import matplotlib.pyplot as plt
cf = pd.tseries.offsets.CustomBusinessDay(weekmask="Sun Mon Tue Wed Thu Fri Sat",
holidays=[d for d in pd.date_range("01-jan-1990",periods=365*50, freq="D")
if d.month!=1])
d = pd.date_range("01-jan-2015", periods=200, freq=cf)
df = pd.DataFrame({"Values":np.random.randint(20,70,len(d))}, index=d)
fig, ax = plt.subplots(2, figsize=[14,6])
df.set_index(df.index.strftime("%Y %d")).plot(ax=ax[0])
df.plot(ax=ax[1])
I suggest that you convert the series to a dataframe and then pivot it to get one column for each year. This lets you plot the data for each year with a separate line, either in the same plot using different colors or in subplots. Here is an example:
import numpy as np # v 1.19.2
import pandas as pd # v 1.2.3
# Create sample series
rng = np.random.default_rng(seed=123) # random number generator
dt = pd.date_range('2018-01-01', '2020-01-31', freq='D')
dt_jan = dt[dt.month == 1]
series = pd.Series(rng.integers(20, 90, size=dt_jan.size), index=dt_jan)
# Convert series to dataframe and pivot it
df_raw = series.to_frame()
df_pivot = df_raw.pivot_table(index=df_raw.index.day, columns=df_raw.index.year)
df = df_pivot.droplevel(axis=1, level=0)
df.head()
# Plot all years together in different colors
ax = df.plot(figsize=(10,4))
ax.set_xlim(1, 31)
ax.legend(frameon=False, bbox_to_anchor=(1, 0.65))
ax.set_xlabel('January', labelpad=10, size=12)
for spine in ['top', 'right']:
ax.spines[spine].set_visible(False)
# Plot years separately
axs = df.plot(subplots=True, color='tab:blue', sharey=True,
figsize=(10,8), legend=None)
for ax in axs:
ax.set_xlim(1, 31)
ax.grid(axis='x', alpha=0.3)
handles, labels = ax.get_legend_handles_labels()
ax.text(28.75, 80, *labels, size=14)
if ax.is_last_row():
ax.set_xlabel('January', labelpad=10, size=12)
ax.figure.subplots_adjust(hspace=0)
For a simple time series:
import pandas as pd
df = pd.DataFrame({'dt':['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-05', '2020-01-06'], 'foo':[1,2, 4,5,6]})
df['dt'] = pd.to_datetime(df.dt)
df['dt_label']= df['dt'].dt.strftime('%Y-%m-%d %a')
df = df.set_index('dt')
#display(df)
df['foo'].plot()
x =plt.xticks(ticks=df.reset_index().dt.values, labels=df.dt_label, rotation=90, horizontalalignment='right')
How can I highlight the x-axis labels for weekends?
edit
Pandas Plots: Separate color for weekends, pretty printing times on x axis
suggests:
def highlight_weekends(ax, timeseries):
d = timeseries.dt
ranges = timeseries[d.dayofweek >= 5].groupby(d.year * 100 + d.weekofyear).agg(['min', 'max'])
for i, tmin, tmax in ranges.itertuples():
ax.axvspan(tmin, tmax, facecolor='orange', edgecolor='none', alpha=0.1)
but applying it with
highlight_weekends(ax, df.reset_index().dt)
will not change the plot
I've extended your sample data a little so we can can make sure that we can highlight more than a single weekend instance.
In this solution I create a column 'weekend', which is a column of bools indicating whether the corresponding date was at a weekend.
We then loop over these values and make a call to ax.axvspan
import pandas as pd
import matplotlib.pyplot as plt
# Add a couple of extra dates to sample data
df = pd.DataFrame({'dt': ['2020-01-01',
'2020-01-02',
'2020-01-04',
'2020-01-05',
'2020-01-06',
'2020-01-07',
'2020-01-09',
'2020-01-10',
'2020-01-11',
'2020-01-12']})
# Fill in corresponding observations
df['foo'] = range(df.shape[0])
df['dt'] = pd.to_datetime(df.dt)
df['dt_label']= df['dt'].dt.strftime('%Y-%m-%d %a')
df = df.set_index('dt')
ax = df['foo'].plot()
plt.xticks(ticks=df.reset_index().dt.values,
labels=df.dt_label,
rotation=90,
horizontalalignment='right')
# Create an extra column which highlights whether or not a date occurs at the weekend
df['weekend'] = df['dt_label'].apply(lambda x: x.endswith(('Sat', 'Sun')))
# Loop over weekend pairs (Saturdays and Sundays), and highlight
for i in range(df['weekend'].sum() // 2):
ax.axvspan(df[df['weekend']].index[2*i],
df[df['weekend']].index[2*i+1],
alpha=0.5)
Here is a solution that uses the fill_between plotting function and the x-axis units so that weekends can be highlighted independently from the DatetimeIndex and the frequency of the data.
The x-axis limits are used to compute the range of time covered by the plot in terms of days, which is the unit used for matplotlib dates. Then a weekends mask is computed and passed to the where argument of the fill_between function. The masks are processed as right-exclusive so in this case, they must contain Mondays for the highlights to be drawn up to Mondays 00:00. Because plotting these highlights can alter the x-axis limits when weekends occur near the limits, the x-axis limits are set back to the original values after plotting.
Note that contrary to axvspan, the fill_between function needs the y1 and y2 arguments. For some reason, using the default y-axis limits leaves a small gap between the plot frame and the tops and bottoms of the weekend highlights. This issue is solved by running ax.set_ylim(*ax.get_ylim()) just after creating the plot.
Here is a complete example based on the provided sample code and using an extended dataset similar to the answer provided by jwalton:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
import matplotlib.dates as mdates
# Create sample dataset
dt = pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-05',
'2020-01-06', '2020-01-07', '2020-01-09', '2020-01-10',
'2020-01-11', '2020-01-14'])
df = pd.DataFrame(dict(foo=range(len(dt))), index=dt)
# Draw pandas plot: setting x_compat=True converts the pandas x-axis units to
# matplotlib date units. This is not necessary for this particular example but
# it is necessary for all cases where the dataframe contains a continuous
# DatetimeIndex (for example ones created with pd.date_range) that uses a
# frequency other than daily
ax = df['foo'].plot(x_compat=True, figsize=(6,4), ylabel='foo')
ax.set_ylim(*ax.get_ylim()) # reset y limits to display highlights without gaps
# Highlight weekends based on the x-axis units
xmin, xmax = ax.get_xlim()
days = np.arange(np.floor(xmin), np.ceil(xmax)+2) # range of days in date units
weekends = [(dt.weekday()>=5)|(dt.weekday()==0) for dt in mdates.num2date(days)]
ax.fill_between(days, *ax.get_ylim(), where=weekends, facecolor='k', alpha=.1)
ax.set_xlim(xmin, xmax) # set limits back to default values
# Create and format x tick for each data point
plt.xticks(df.index.values, df.index.strftime('%d\n%a'), rotation=0, ha='center')
plt.title('Weekends are highlighted from SAT 00:00 to MON 00:00', pad=15, size=12);
You can find more examples of this solution in the answers I have posted here and here.
I am trying to plot a multiple time series dataframe in pandas. The time series is a 1 year daily points of length 365. The figure is coming alright but I want to suppress the year tick showing on the x axis.
I want to suppress the 1950 label showing in the left corner of x axis. Can anybody suggest something on this? My code
dates = pandas.date_range('1950-01-01', '1950-12-31', freq='D')
data_to_plot12 = pandas.DataFrame(data=data_array, # values
index=homo_regions) # 1st column as index
dataframe1 = pandas.DataFrame.transpose(data_to_plot12)
dataframe1.index = dates
ax = dataframe1.plot(lw=1.5, marker='.', markersize=2, title='PRECT time series PI Slb Ocn CNTRL 60 years')
ax.set(xlabel="Months", ylabel="PRECT (mm/day)")
fig_name = 'dataframe1.pdf'
plt.savefig(fig_name)
You should be able to specify the xaxis major formatter like so
import matplotlib.dates as mdates
...
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))