Develop Reference

How to draw histogram + QQ plots together for each column?

I have a dataset with lots of numerical columns. I want to draw histogram for each column but also add extra QQ plot just to check more thoroughly if data follow normal distribution. So I would like to have histogram and QQ plot under histogram for each column. Something like that:
I tried to do this using following code but both plots overlap each other:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
num_cols = df.select_dtypes(include=np.number)
cols = num_cols.columns.tolist()
df_sample = df.sample(n=5000)
fig, axes = plt.subplots(4, 5, figsize=(15,12), layout = 'constrained')
for col, axs in zip(cols, axes.flat):
sns.histplot(data = df_sample[col], kde = True, stat = 'density', ax = axs, alpha = .4)
sm.qqplot(df_sample[col], line='45', ax = axs)
plt.show()
How can I generate hist and QQ plots one under another for each column?
Another issue is that my QQ plots look strange, I'm wondering if I need to standarize all my columns before making QQ plot.

How to change the legend font size of pd.DataFrame.plot() when `secondary_y` is used?

Question
I have used the secondary_y argument in pd.DataFrame.plot().
While trying to change the fontsize of legends by .legend(fontsize=20), I ended up having only 1 column name in the legend when I actually have 2 columns to be printed on the legend.
This problem (having only 1 column name in the legend) does not take place when I did not use secondary_y argument.
I want all the column names in my dataframe to be printed in the legend, and change the fontsize of the legend even when I use secondary_y while plotting dataframe.
Example
The following example with secondary_y shows only 1 column name A, when I have actually 2 columns, which are A and B.
The fontsize of the legend is changed, but only for 1 column name.
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.DataFrame(np.random.randn(24*3, 2),
index=pd.date_range('1/1/2019', periods=24*3, freq='h'))
df.columns = ['A', 'B']
df.plot(secondary_y = ["B"], figsize=(12,5)).legend(fontsize=20, loc="upper right")
When I do not use secondary_y, then legend shows both of the 2 columns A and B.
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.DataFrame(np.random.randn(24*3, 2),
index=pd.date_range('1/1/2019', periods=24*3, freq='h'))
df.columns = ['A', 'B']
df.plot(figsize=(12,5)).legend(fontsize=20, loc="upper right")

To manage to customize it you have to create your graph with subplots function of Matplotlib:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)
df = pd.DataFrame(np.random.randn(24*3, 2),
index=pd.date_range('1/1/2019', periods=24*3, freq='h'))
df.columns = ['A', 'B']
#define colors to use
col1 = 'steelblue'
col2 = 'red'
#define subplots
fig,ax = plt.subplots()
#add first line to plot
lns1=ax.plot(df.index,df['A'], color=col1)
#add x-axis label
ax.set_xlabel('dates', fontsize=14)
#add y-axis label
ax.set_ylabel('A', color=col1, fontsize=16)
#define second y-axis that shares x-axis with current plot
ax2 = ax.twinx()
#add second line to plot
lns2=ax2.plot(df.index,df['B'], color=col2)
#add second y-axis label
ax2.set_ylabel('B', color=col2, fontsize=16)
#legend
ax.legend(lns1+lns2,['A','B'],loc="upper right",fontsize=20)
#another solution is to create legend for fig,:
#fig.legend(['A','B'],loc="upper right")
plt.show()
result:

this is a somewhat late response, but something that worked for me was simply setting plt.legend(fontsize = wanted_fontsize) after the plot function.

How can I plot slice of certain DataFrame for each row with different color?

I would like to plot certain slices of my Pandas Dataframe for each rows (based on row indexes) with different colors.
My data look like the following:
I already tried with the help of this tutorial to find a way but I couldn't - probably due to a lack of skills.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("D:\SOF10.csv" , header=None)
df.head()
#Slice interested data
C = df.iloc[:, 2::3]
#Plot Temp base on row index colorfully
C.apply(lambda x: plt.scatter(x.index, x, c='g'))
plt.show()
Following is my expected plot:
I was also wondering if I could displace the mean of each row of the sliced data which contains 480 values somewhere in the plot or in the legend beside of plot! Is it feasible (like the following picture) to calculate the mean and displaced somewhere in the legend or by using small font size displace next to its own data in graph ?
Data sample: data

This gives the plot without legend
C = df.iloc[:,2::3].stack().reset_index()
C.columns = ['level_0', 'level_1', 'Temperature']
fig, ax = plt.subplots(1,1)
C.plot('level_0', 'Temperature',
ax=ax, kind='scatter',
c='level_0', colormap='tab20',
colorbar=False, legend=True)
ax.set_xlabel('Cycles')
plt.show()
Edit to reflect modified question:
stack() transform your (sliced) dataframe to a series with index (row, col)
reset_index() reset the double-level index above to level_0 (row), level_1 (col).
set_xlabel sets the label of x-axis to what you want.
Edit 2: The following produces scatter with legend:
CC = df.iloc[:,2::3]
fig, ax = plt.subplots(1,1, figsize=(16,9))
labels = CC.mean(axis=1)
for i in CC.index:
ax.scatter([i]*len(CC.columns[1:]), CC.iloc[i,1:], label=labels[i])
ax.legend()
ax.set_xlabel('Cycles')
ax.set_ylabel('Temperature')
plt.show()

This may be an approximate answer. scatter(c=, cmap= can be used for desired coloring.
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import itertools
df = pd.DataFrame({'a':[34,22,1,34]})
fig, subplot_axes = plt.subplots(1, 1, figsize=(20, 10)) # width, height
colors = ['red','green','blue','purple']
cmap=matplotlib.colors.ListedColormap(colors)
for col in df.columns:
subplot_axes.scatter(df.index, df[col].values, c=df.index, cmap=cmap, alpha=.9)

Plotting multiple lines grouped by one column dataframe, with date time as x axis [duplicate]

In Pandas, I am doing:
bp = p_df.groupby('class').plot(kind='kde')
p_df is a dataframe object.
However, this is producing two plots, one for each class.
How do I force one plot with both classes in the same plot?

Version 1:
You can create your axis, and then use the ax keyword of DataFrameGroupBy.plot to add everything to these axes:
import matplotlib.pyplot as plt
p_df = pd.DataFrame({"class": [1,1,2,2,1], "a": [2,3,2,3,2]})
fig, ax = plt.subplots(figsize=(8,6))
bp = p_df.groupby('class').plot(kind='kde', ax=ax)
This is the result:
Unfortunately, the labeling of the legend does not make too much sense here.
Version 2:
Another way would be to loop through the groups and plot the curves manually:
classes = ["class 1"] * 5 + ["class 2"] * 5
vals = [1,3,5,1,3] + [2,6,7,5,2]
p_df = pd.DataFrame({"class": classes, "vals": vals})
fig, ax = plt.subplots(figsize=(8,6))
for label, df in p_df.groupby('class'):
df.vals.plot(kind="kde", ax=ax, label=label)
plt.legend()
This way you can easily control the legend. This is the result:

import matplotlib.pyplot as plt
p_df.groupby('class').plot(kind='kde', ax=plt.gca())

Another approach would be using seaborn module. This would plot the two density estimates on the same axes without specifying a variable to hold the axes as follows (using some data frame setup from the other answer):
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# data to create an example data frame
classes = ["c1"] * 5 + ["c2"] * 5
vals = [1,3,5,1,3] + [2,6,7,5,2]
# the data frame
df = pd.DataFrame({"cls": classes, "indices":idx, "vals": vals})
# this is to plot the kde
sns.kdeplot(df.vals[df.cls == "c1"],label='c1');
sns.kdeplot(df.vals[df.cls == "c2"],label='c2');
# beautifying the labels
plt.xlabel('value')
plt.ylabel('density')
plt.show()
This results in the following image.

There are two easy methods to plot each group in the same plot.
When using pandas.DataFrame.groupby, the column to be plotted, (e.g. the aggregation column) should be specified.
Use seaborn.kdeplot or seaborn.displot and specify the hue parameter
Using pandas v1.2.4, matplotlib 3.4.2, seaborn 0.11.1
The OP is specific to plotting the kde, but the steps are the same for many plot types (e.g. kind='line', sns.lineplot, etc.).
Imports and Sample Data
For the sample data, the groups are in the 'kind' column, and the kde of 'duration' will be plotted, ignoring 'waiting'.
import pandas as pd
import seaborn as sns
df = sns.load_dataset('geyser')
# display(df.head())
duration waiting kind
0 3.600 79 long
1 1.800 54 short
2 3.333 74 long
3 2.283 62 short
4 4.533 85 long
Plot with pandas.DataFrame.plot
Reshape the data using .groupby or .pivot
.groupby
Specify the aggregation column, ['duration'], and kind='kde'.
ax = df.groupby('kind')['duration'].plot(kind='kde', legend=True)
.pivot
ax = df.pivot(columns='kind', values='duration').plot(kind='kde')
Plot with seaborn.kdeplot
Specify hue='kind'
ax = sns.kdeplot(data=df, x='duration', hue='kind')
Plot with seaborn.displot
Specify hue='kind' and kind='kde'
fig = sns.displot(data=df, kind='kde', x='duration', hue='kind')
Plot

Maybe you can try this:
fig, ax = plt.subplots(figsize=(10,8))
classes = list(df.class.unique())
for c in classes:
df2 = data.loc[data['class'] == c]
df2.vals.plot(kind="kde", ax=ax, label=c)
plt.legend()

Python plotting by different dataframe columns (using Seaborn?)

I'm trying to create a scatterplot of a dataset with point coloring based on different categorical columns. Seaborn works well here for one plot:
fg = sns.FacetGrid(data=plot_data, hue='col_1')
fg.map(plt.scatter, 'x_data', 'y_data', **kws).add_legend()
plt.show()
I then want to display the same data, but with hue='col_2' and hue='col_3'. It works fine if I just make 3 plots, but I'm really hoping to find a way to have them all appear as subplots in one figure. Unfortunately, I haven't found any way to change the hue from one plot to the next. I know there are plotting APIs that allow for an axis keyword, thereby letting you pop it into a matplotlib figure, but I haven't found one that simultaneously allows you to set 'ax=' and 'hue='. Any ideas?
Thanks in advance!
Edit:
Here's some sample code to illustrate the idea
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
sns.set(style='ticks')
fg = sns.FacetGrid(data=df, hue='pet', size=5)
fg.map(plt.scatter, 'x', 'y').add_legend()
fg = sns.FacetGrid(data=df, hue='hair', size=5)
fg.map(plt.scatter, 'x', 'y').add_legend()
plt.show()
This plots what I want, but in two windows. The color scheme is set in the first plot by grouping by 'pet', and in the second plot by 'hair'. Is there any way to do this on one plot?

In order to plot 3 scatterplots with different colors for each, you may create 3 axes in matplotlib and plot a scatter to each axes.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(10,5),
columns=["x", "y", "col1", "col2", "col3"])
fig, axes = plt.subplots(nrows=3)
for ax, col in zip(axes, df.columns[2:]):
ax.scatter(df.x, df.y, c=df[col])
plt.show()
For categorical data it is often easier to plot several scatter plots, one per category.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import seaborn as sns
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
cols = ['pet',"hair"]
fig, axes = plt.subplots(nrows=len(cols ))
for ax,col in zip(axes,cols):
for n, group in df.groupby(col):
ax.scatter(group.x,group.y, label=n)
ax.legend()
plt.show()
You may surely use a FacetGrid, if you really want, but that requires a different data format of the DataFrame.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import seaborn as sns
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
df2 = pd.melt(df, id_vars=['x','y'], value_name='category', var_name="kind")
fg = sns.FacetGrid(data=df2, row="kind",hue='category', size=3)
fg.map(plt.scatter, 'x', 'y').add_legend()