Iterating and ploting five columns per iteration pandas - pandas

Question

I am trying to plot five columns per iteration, but current code is ploting everithing five times. How to explain to it to plot five columns per iteration without repeting them?
n=4
for tag_1,tag_2,tag_3,tag_4,tag_5 in zip(df.columns[n:], df.columns[n+1:], df.columns[n+2:], df.columns[n+3:], df.columns[n+4:]):
fig,ax=plt.subplots(ncols=5, tight_layout=True, sharey=True, figsize=(20,3))
sns.scatterplot(df, x=tag_1, y='variable', ax=ax[0])
sns.scatterplot(df, x=tag_2, y='variable', ax=ax[1])
sns.scatterplot(df, x=tag_3, y='variable', ax=ax[2])
sns.scatterplot(df, x=tag_4, y='variable', ax=ax[3])
sns.scatterplot(df, x=tag_5, y='variable', ax=ax[4])
plt.show()

Answer 1

You are using list slicing in the wrong way. When you use df.columns[n:], you are getting all the column names from the one with index n to the last one. The same is valid for n+1, n+2, n+3 and n+4. This causes the repetition that you are referring to. In addition to that, the fact that the plot is shown five times is due to the behavior of the zip function: when used on iterables with different sizes, the iterable returned by zip has the size of the smaller one (in this case df.columns[n+4:]).
You can achieve what you want by adapting your code as follows:
# Imports to create sample data
import string
import random
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Create some sample data and a sample dataframe
data = { string.ascii_lowercase[i]: [random.randint(0, 100) for _ in range(100)] for i in range(15) }
df = pd.DataFrame(data)
# Iterate in groups of five indexes
for start in range(0, len(df.columns), 5):
# Get the next five columns. Pay attention to the case in which the number of columns is not a multiple of 5
cols = [df.columns[idx] for idx in range(start, min(start+5, len(df.columns)))]
# Adapt your plot and take into account that the last group can be smaller than 5
fig,ax=plt.subplots(ncols=len(cols), tight_layout=True, sharey=True, figsize=(20,3))
for idx in range(len(cols)):
#sns.scatterplot(df, x=cols[idx], y='variable', ax=ax[idx])
sns.scatterplot(df, x=cols[idx], y=df[cols[idx]], ax=ax[idx]) # In the example the values of the column are plotted
plt.show()
In this case, the code performs the following steps:
Iterate over groups of at most five indexes ([0->4], [5->10]...)
Recover the columns that are positioned in the previously recovered indexes. The last group of columns may be smaller than 5 (e.g., 18 columns, the last is composed of the ones with the following indexes: 15, 16, 17
Create the plot taking into account the previous corner case of less than 5 columns

Answer 2

With Seaborn's object interface, available from v0.12, we might do like this:
from numpy import random
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn.objects as so
sns.set_theme()
First, let's create a sample dataset, just like trolloldem's answer.
random.seed(0) # To produce the same random values across multiple runs
columns = list("abcdefghij")
sample_size = 20
df_orig = pd.DataFrame(
{c: random.randint(100, size=sample_size) for c in columns},
index=pd.Series(range(sample_size), name="variable")
)
Then transform the data frame into a long-form for easier processing.
df = (df_orig
.melt(value_vars=columns, var_name="tag", ignore_index=False)
.reset_index()
)
Then finally render the figures, 5 figures per row.
(
so.Plot(df, x="value", y="variable") # Or you might do x="variable", y="value" instead
.facet(col="tag", wrap=5)
.add(so.Dot())
)