I have a dataframe which stores the number of clients, predicted revenue, and actual revenue for a discrete set of products. I would like to plot a combo chart with number of clients on the first y axis as a bar plot, and both predicted and actual revenue plotted on the second y axis with the same scale.
I'm able to create a combo chart with a single secondary y axis using the following:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({
'product' : ['A','B','C','D'],
'number_of_clients' : [234,473,325,389],
'pred_turnover' : [1287,2311,5283,3211],
'act_turnover' : [1221,1927,5433,3888]})
df['number_of_clients'].plot.bar()
df['pred_turnover'].plot(secondary_y=True)
However, I am stuck on how to add a second variable to the secondary y axis using the same scale.
Here is what I would like to create as an end product:
I rewrote it in the general format instead of the df.plot format.
The point is that ax1=axtwinx() is a biaxial graph, and the difference between the maximum and minimum on the right axis is divided by the number of ticks on the left axis to adjust the ticks.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({
'product' : ['A','B','C','D'],
'number_of_clients' : [234,473,325,389],
'pred_turnover' : [1287,2311,5283,3211],
'act_turnover' : [1221,1927,5433,3888]})
fig, ax = plt.subplots(figsize=(8,4))
ax.bar(df['product'], df['number_of_clients'], label='number_of_clients')
ax1 = ax.twinx()
ax1.plot(df['product'], df['pred_turnover'], lw=2, color='orange', label='pred_product')
ax1.set_yticks(np.arange(ax1.get_yticks()[0], ax1.get_yticks()[-1], (ax1.get_yticks()[-1] - ax1.get_yticks()[0])/(len(ax.get_yticks())-1)))
ax.grid(which='major', axis='y')
plt.show()
Related
I have a dataframe and I'm using seaborn pairplot to plot one target column vs the rest of the columns.
Code is below,
import seaborn as sns
import matplotlib.pyplot as plt
tgt_var = 'AB'
var_lst = ['A','GH','DL','GT','MS']
pp = sns.pairplot(data=df,
y_vars=[tgt_var],
x_vars=var_lst)
pp.fig.set_figheight(6)
pp.fig.set_figwidth(20)
The var_lst is not a static list, I just provided an example.
What I need is to plot tgt_var on Y axis and each var_lst on x axis.
I'm able to do this with above code, but I also want to use log scale on X axis only if the var_lst item is 'GH' or 'MS', for the rest normal scale. Is there any way to achieve this?
Iterate pp.axes.flat and set xscale="log" if the xlabel matches "GH" or "MS":
log_columns = ["GH", "MS"]
for ax in pp.axes.flat:
if ax.get_xlabel() in log_columns:
ax.set(xscale="log")
Full example with the iris dataset where the petal columns are xscale="log":
import seaborn as sns
df = sns.load_dataset("iris")
pp = sns.pairplot(df)
log_columns = ["petal_length", "petal_width"]
for ax in pp.axes.flat:
if ax.get_xlabel() in log_columns:
ax.set(xscale="log")
My data is in a dataframe of two columns: y and x. The data refers to the past few years. Dummy data is below:
np.random.seed(167)
rng = pd.date_range('2017-04-03', periods=365*3)
df = pd.DataFrame(
{"y": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365*3)]),
"x": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365*3)])
}, index=rng
)
In first attempt, I plotted a scatterplot with Seaborn using the following code:
import seaborn as sns
import matplotlib.pyplot as plt
def plot_scatter(data, title, figsize):
fig, ax = plt.subplots(figsize=figsize)
ax.set_title(title)
sns.scatterplot(data=data,
x=data['x'],
y=data['y'])
plot_scatter(data=df, title='dummy title', figsize=(10,7))
However, I would like to generate a 4x3 matrix including 12 scatterplots, one for each month with year as hue. I thought I could create a third column in my dataframe that tells me the year and I tried the following:
import seaborn as sns
import matplotlib.pyplot as plt
def plot_scatter(data, title, figsize):
fig, ax = plt.subplots(figsize=figsize)
ax.set_title(title)
sns.scatterplot(data=data,
x=data['x'],
y=data['y'],
hue=data.iloc[:, 2])
df['year'] = df.index.year
plot_scatter(data=df, title='dummy title', figsize=(10,7))
While this allows me to see the years, it still shows all the data in the same scatterplot instead of creating multiple scatterplots, one for each month, so it's not offering the level of detail I need.
I could slice the data by month and build a for loop that plots one scatterplot per month but I actually want a matrix where all the scatterplots use similar axis scales. Does anyone know an efficient way to achieve that?
To create multiple subplots at once, seaborn introduces figure-level functions. The col= argument indicates which column of the dataframe should be used to identify the subplots. col_wrap= can be used to tell how many subplots go next to each other before starting an additional row.
Note that you shouldn't create a figure, as the function creates its own new figure. It uses the height= and aspect= arguments to tell the size of the individual subplots.
The code below uses a sns.relplot() on the months. An extra column for the months is created; it is made categorical to fix an order.
To remove the month= in the title, you can loop through the generated axes (a recent seaborn version is needed for axes_dict). With sns.set(font_scale=...) you can change the default sizes of all texts.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(167)
dates = pd.date_range('2017-04-03', periods=365 * 3, freq='D')
df = pd.DataFrame({"y": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365 * 3)]),
"x": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365 * 3)])
}, index=dates)
df['year'] = df.index.year
month_names = pd.date_range('2017-01-01', periods=12, freq='M').strftime('%B')
df['month'] = pd.Categorical.from_codes(df.index.month - 1, month_names)
sns.set(font_scale=1.7)
g = sns.relplot(kind='scatter', data=df, x='x', y='y', hue='year', col='month', col_wrap=4, height=4, aspect=1)
# optionally remove the `month=` in the title
for name, ax in g.axes_dict.items():
ax.set_title(name)
plt.setp(g.axes, xlabel='', ylabel='') # remove all x and y labels
g.axes[-2].set_xlabel('x', loc='left') # set an x label at the left of the second to last subplot
g.axes[4].set_ylabel('y') # set a y label to 5th subplot
plt.subplots_adjust(left=0.06, bottom=0.06) # set some more spacing at the left and bottom
plt.show()
I'm a beginner in Python.
In my internship project I am trying to plot bloxplots from data contained in a csv
I need to plot bloxplots for each of the 4 (four) variables showed above (AAG, DENS, SRG e RCG). Since each variable presents values in the range from [001] to [100], there will be 100 boxplots for each variable, which need to be plotted in a single graph as shown in the image.
This is the graph I need to plot, but for each variable there will be 100 bloxplots as each one has 100 columns of values:
The x-axis is the "Year", which ranges from 2025 to 2030, so I need a graph like the one shown in figure 2 for each year and the y-axis is the sets of values for each variable.
Using Pandas-melt function and seaborn library I was able to plot only the boxplots of a column. But that's not what I need:
import pandas as pd
import seaborn as sns
df = pd.read_csv("2DBM_50x50_Central_Aug21_Sim.cliped.csv")
mdf= df.melt(id_vars=['Year'], value_vars='AAG[001]')
print(mdf)
ax=sns.boxplot(x='Year', y='value',width = 0.2, data=mdf)
Result of the code above:
What can I try to resolve this?
The following code gives you five subplots, where each subplot only contains the data of one variable. Then a boxplot is generated for each year. To change the range of columns used for each variable, change the upper limit in var_range = range(1, 101), and to see the outliers change showfliers to True.
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv("2DBM_50x50_Central_Aug21_Sim.cliped.csv")
variables = ["AAG", "DENS", "SRG", "RCG", "Thick"]
period = range(2025, 2031)
var_range = range(1, 101)
fig, axes = plt.subplots(2, 3)
flattened_axes = fig.axes
flattened_axes[-1].set_visible(False)
for i, var in enumerate(variables):
var_columns = [f"TB_acc_{var}[{j:05}]" for j in var_range]
data = df.melt(id_vars=["Period"], value_vars=var_columns, value_name=var)
ax = flattened_axes[i]
sns.boxplot(x="Period", y=var, width=0.2, data=data, ax=ax, showfliers=False)
plt.tight_layout()
plt.show()
output:
I have the following dataset, code and plot:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
data = [['tom', 10,15], ['matt', 13,10]]
df3 = pd.DataFrame(data, columns = ['Name', 'Attempts','L4AverageAttempts'])
f,ax = plt.subplots(nrows=1,figsize=(16,9))
sns.barplot(x='Attempts',y='Name',data=df3)
plt.show()
How can get a marker of some description (dot, *, shape, etc) to show that tomhas averaged 15 (so is below his average) and matt has averaged 10 so is above average. So a marker basxed off the L4AverageAttempts value for each person.
I have looked into axvline but that seems to be only a set number rather than a specific value for each y axis category. Any help would be much appreciated! thanks!
You can simply plot a scatter plot on top of your bar plot using L4AverageAttempts as the x value:
You can use seaborn.scatterplot for this. Make sure to set the zorder parameter so that the markers appear on top of the bars.
import seaborn as sns
import pandas as pd
data = [['tom', 10,15], ['matt', 13,10]]
df3 = pd.DataFrame(data, columns = ['Name', 'Attempts','L4AverageAttempts'])
f,ax = plt.subplots(nrows=1,figsize=(16,9))
sns.barplot(x='Attempts',y='Name',data=df3)
sns.scatterplot(x='L4AverageAttempts', y="Name", data=df3, zorder=10, color='k', edgecolor='k')
plt.show()
I have a monthly time series data (5min) in which each day is a column and each 5min is a row so the shape is (288,30). I would like to plot all the data as thin lines with low alpha. Also, on the same graph I would like to plot the maximum values on their respective time ID as thick dots, to illustrate where they occur.
I have tried the code below but cannot link the timeindex axis with those ID of the maximum values.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.randint(0,100,size=(8640, 1)),index=pd.date_range(start="20180301", freq='5T',periods=8640),columns=['A'])
df_all_days=df.groupby(df.index.time).aggregate(lambda x:list(x))
df_all_days_exp=df_all_days.apply(pd.Series)
df_all_days_exp_Max=df_all_days_exp.max(axis=0)
df_all_days_exp_MaxID=df_all_days_exp.idxmax(axis=0)
df_all_days_exp_Max_ID=pd.DataFrame([df_all_days_exp_Max,df_all_days_exp_MaxID]).T
plt.figure()
plt.plot(df_all_days_exp,linewidth=0.3,alpha=0.4)
plt.plot(df_all_days_exp_Max_ID,'.k',linewidth=1.5)
IIUC, you can try:
hour_df = df.resample('H').max()
fig, ax = plt.subplots(figsize=(10,6))
df.plot(ax=ax, alpha=0.1)
ax2 = ax.twiny()
ax2.scatter(range(len(hour_df)), hour_df, c='r')
ax2.set_xticks([])
plt.show()
Output: