According to the following answer I should be able to use subplots with multiindex to show pairwise plots: Pandas Plotting with Multi-Index
But this seems not to work for me.
This is an example:
import pandas as pd
import numpy as np
# prepare example data
adf = pd.DataFrame(index = pd.date_range('2019-01-01', periods=30),
data= np.random.randint(0,100,(30,2)), columns=['X','Y'])
bdf = pd.DataFrame(index = pd.date_range('2019-01-01', periods=30),
data= np.random.randint(0,100,(30,2)), columns=['X','Y'])
df = pd.concat({'a': adf, 'b': bdf}).unstack(level=0)
# plot
_ = df.plot(kind='line', subplots=True, figsize=(10, 10))
The result is 4 plots for each column.
But I want 2 pairwise plots like this:
I can achieve this with the following line:
_ = [df.loc[:,df.columns.get_level_values(0) == c].plot() for c in df.columns.get_level_values(0).unique()]
But should this not be possible with multiindex and subplots feature?
Related
The column class has 2 options for the value, either 'b' or 's'. I am trying to display a graph/chart that shows how many are 'b' and how many are 's'. I can't figure out how to do this when they are both in the same column.
The current code shows a scatter plot, but I'd like to use the data from column 'class'.
import pandas as pd
import matplotlib.pylab as plt
import numpy as np
#df = df.groupby('class')['class'].count()
#print(df)
df = pd.DataFrame(np.random.randint(0,10,size=(5, 2)), columns=['x','y'])
df['class'] = ['Benign','Malware','Benign','Malware','Malware']
# plot groupby results on the same canvas
fig, ax = plt.subplots(figsize=(8,6))
for n, grp in df.groupby('class'):
ax.scatter(x = "x", y = "y", data=grp, label=n)
ax.legend(title="Label")
plt.show()
Don't group by class and loop, rather aggregate (with value_counts) then plot:
df['class'].value_counts().plot.bar()
or with matplotlib's functions:
s = df['class'].value_counts()
ax.bar(s.index, s)
output:
I was trying to plot multiple lmplots in the same figure. But I am getting too many unwanted subplots.
I found another SO link How to plot 2 seaborn lmplots side-by-side? but that also did not help me.
In this example I want 1 row 2 columns.
MWE
# imports
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# data
df = sns.load_dataset('titanic')
# plot
m,n = 1,2
figsize=(12,8)
cols1 = ['age','fare']
cols2 = ['fare','age']
target = 'survived'
fontsize = 12
fig, ax = plt.subplots(m,n,figsize=figsize)
for i, (col1,col2) in enumerate(zip(cols1,cols2)):
plt.subplot(m,n,i+1)
sns.lmplot(x=col1,y=col2,data=df,
hue=target, palette='Set1',
scatter_kws={'alpha':0.3})
plt.xlabel(col1,fontsize=fontsize)
plt.ylabel(col2,fontsize=fontsize)
plt.tick_params(axis='both', which='major', labelsize=fontsize)
plt.tight_layout()
for i in range(m*n-len(cols1)):
ax.flat[-(i+1)].set_visible(False)
My attempt so far:
df = pd.DataFrame({'x0':[10,20,30,40],
'y0': [100,200,300,400],
'x1':[0.1,0.2,0.3,0.1],
'y1':[0.01,0.02,0.03,0.01],
'target': [0,1,1,1]
})
df1 = df.append(df)
df1 = df1.reset_index(drop=True)
df1['x0'].iloc[len(df):] = df['x1'].to_numpy()
df1['y0'].iloc[len(df):] = df['y1'].to_numpy()
df1['col'] = ['c0']* len(df) + ['c1'] * len(df)
df1 = df1.drop(['x1','y1'],axis=1)
df1 = df1.rename(columns={'x0':'x','y0':'y'})
sns.lmplot(x='x',y='y',hue='target',data=df1,col='col')
Output:
I have a dictionary of dataframes. I am trying to count the rows in each dataframe. For the real data, my code is counting just over ten thousand rows for a dataframe that has only has a few rows.
I have tried to reproduce the error using dummy data. Unfortunately, the code works fine with the dummy data!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Dataframe
Df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))
# Map
Ma = Df.groupby('D')
# Dictionary of Dataframes
Di = {}
for name, group in Ma:
Di[str(name)] = group
# Count the Rows in each Dataframe
Li = []
for k in Di:
Count = Di[k].shape[0]
Li.append([Count])
# Flatten
Li_1 = []
for sublist in Li:
for item in sublist:
Li_1.append(item)
# Histogram
plt.hist(Li_1, bins=10)
plt.xlabel("Rows / Dataframe")
plt.ylabel("Frequency")
fig = plt.gcf()
To get the number of rows corresponding to each category in 'D', you can simply use .size when you do your groupby:
Df.groupby('D').size()
pandas also allows you to directly plot graphs, so your code can be reduced to:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))
Df.groupby('D').size().plot.hist()
plt.xlabel("Rows / Dataframe")
plt.ylabel("Frequency")
fig = plt.gcf()
Assuming that, the data in column D is a categorical variable. You can get the count for each category using seaborn countplot.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Dataframe
df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))
# easy count plot in sns
sns.countplot(x='D',data=df)
plt.xlabel("category")
plt.ylabel("frequency")
But if you are looking for distribution plot but not categorical count plot then you can use the folowing part of the code to have distribution plot.
# for distribution plot
sns.distplot(df['D'],kde=False,bins=10)
plt.xlabel("Spread")
plt.ylabel("frequency")
But if you want distribution plot after group by the elements which does not make any sense to me but you can use the following:
# for distribution plot after group by
sns.distplot(df.groupby('D').size() ,kde=False,bins=10)
plt.xlabel("Spread")
plt.ylabel("frequency")
I have a Dataframe and I slice the Dataframe into three subsets. Each subset has 3 to 4 rows of data. After I slice the data frame into three subsets, I plot them using Matplotlib.
The problem I have is I am not able to create a plot where each subplot is plotted using sliced DataFrame. For example, in a group of three in a set, I have only one of the plots (last subplot) plotted where there is no data for the remaining two plots initial sets in a group. it looks like the 'r' value does not pass to 'r.plot' for all three subplots.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
df['key1'] = 0
df.key1.iloc[0:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:] = 3
df_grouped = df.groupby('key1')
for group_name, group_value in df_grouped:
rows, columns = group_value.shape
fig, axes = plt.subplots(rows, 1, sharex=True, sharey=True, figsize=(15,20))
for i,r in group_value.iterrows():
r = r[0:columns-1]
r.plot(kind='bar', fill=False, log=False)
I think you might want what I call df_subset to be summarized in some way, but here's a way to plot each group in its own panel.
# Your Code Setting Up the Dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
df['key1'] = 0
df.key1.iloc[0:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:] = 3
# My Code to Plot in Three Panels
distinct_keys = df['key1'].unique()
fig, axes = plt.subplots(len(distinct_keys), 1, sharex=True, figsize=(3,5))
for i, key in enumerate(distinct_keys):
df_subset = df[df.key1==key]
# {maybe insert a line here to summarize df_subset somehow interesting?}
# plot
axes[i] = df_subset.plot(kind='bar', fill=False, log=False)
One year of sample data:
import pandas as pd
import numpy.random as rnd
import seaborn as sns
n = 365
df = pd.DataFrame(data = {"A":rnd.randn(n), "B":rnd.randn(n)+1},
index=pd.date_range(start="2017-01-01", periods=n, freq="D"))
I want to boxplot these data side-by-side grouped by the month (i.e., two boxes per month, one for A and one for B).
For a single column sns.boxplot(df.index.month, df["A"]) works fine. However, sns.boxplot(df.index.month, df[["A", "B"]]) throws an error (ValueError: cannot copy sequence with size 2 to array axis with dimension 365). Melting the data by the index (pd.melt(df, id_vars=df.index, value_vars=["A", "B"], var_name="column")) in order to use seaborn's hue property as a workaround doesn't work either (TypeError: unhashable type: 'DatetimeIndex').
(A solution doesn't necessarily need to use seaborn, if it is easier using plain matplotlib.)
Edit
I found a workaround that basically produces what I want. However, it becomes somewhat awkward to work with once the DataFrame includes more variables than I want to plot. So if there is a more elegant/direct way to do it, please share!
df_stacked = df.stack().reset_index()
df_stacked.columns = ["date", "vars", "vals"]
df_stacked.index = df_stacked["date"]
sns.boxplot(x=df_stacked.index.month, y="vals", hue="vars", data=df_stacked)
Produces:
here's a solution using pandas melting and seaborn:
import pandas as pd
import numpy.random as rnd
import seaborn as sns
n = 365
df = pd.DataFrame(data = {"A": rnd.randn(n),
"B": rnd.randn(n)+1,
"C": rnd.randn(n) + 10, # will not be plotted
},
index=pd.date_range(start="2017-01-01", periods=n, freq="D"))
df['month'] = df.index.month
df_plot = df.melt(id_vars='month', value_vars=["A", "B"])
sns.boxplot(x='month', y='value', hue='variable', data=df_plot)
month_dfs = []
for group in df.groupby(df.index.month):
month_dfs.append(group[1])
plt.figure(figsize=(30,5))
for i,month_df in enumerate(month_dfs):
axi = plt.subplot(1, len(month_dfs), i + 1)
month_df.plot(kind='box', subplots=False, ax = axi)
plt.title(i+1)
plt.ylim([-4, 4])
plt.show()
Will give this
Not exactly what you're looking for but you get to keep a readable DataFrame if you add more variables.
You can also easily remove the axis by using
if i > 0:
y_axis = axi.axes.get_yaxis()
y_axis.set_visible(False)
in the loop before plt.show()
This is quite straight-forward using Altair:
alt.Chart(
df.reset_index().melt(id_vars = ["index"], value_vars=["A", "B"]).assign(month = lambda x: x["index"].dt.month)
).mark_boxplot(
extent='min-max'
).encode(
alt.X('variable:N', title=''),
alt.Y('value:Q'),
column='month:N',
color='variable:N'
)
The code above melts the DataFrame and adds a month column. Then Altair creates box-plots for each variable broken down by months as the plot columns.