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This online Python compiler runs real Python, including NumPy and Pandas, inside your browser tab using WebAssembly. There is nothing to install and no environment to set up, so you can practice Python for data analysis straight away.
Write some code, press Run, and see the output. No account is needed.
collections.Countercollections.Use variables, f-strings and a loop to summarize a list of scores.
name = "Skillancy"
scores = [72, 85, 90, 64]
average = sum(scores) / len(scores)
print(f"{name} average score: {average:.1f}")
for s in scores:
print(s, "pass" if s >= 70 else "fail")Transform a list with a comprehension and count repeated words with collections.Counter.
from collections import Counter
words = "data analytics needs clean data and clear questions".split()
lengths = [len(w) for w in words]
print(lengths)
print(Counter(words).most_common(2))Write a function that removes duplicates while preserving the original order.
def dedupe(items):
seen = set()
result = []
for item in items:
if item not in seen:
seen.add(item)
result.append(item)
return result
print(dedupe([3, 1, 3, 2, 1, 4]))Build a DataFrame and summarize sales by city with groupby and aggregation.
import pandas as pd
df = pd.DataFrame({
"city": ["Pune", "Delhi", "Pune", "Chennai", "Delhi"],
"sales": [120, 90, 150, 60, 110],
})
print(df.groupby("city")["sales"].agg(["sum", "mean"]))Join two DataFrames with the Pandas equivalent of a SQL LEFT JOIN.
import pandas as pd
orders = pd.DataFrame({"order_id": [1, 2, 3], "customer_id": [10, 11, 10], "amount": [250, 400, 150]})
customers = pd.DataFrame({"customer_id": [10, 11, 12], "name": ["Asha", "Ravi", "Meera"]})
merged = orders.merge(customers, on="customer_id", how="left")
print(merged)Find missing values and fill numeric and text columns with appropriate replacements.
import numpy as np
import pandas as pd
df = pd.DataFrame({
"age": [25, np.nan, 31, np.nan, 40],
"city": ["Pune", "Delhi", None, "Delhi", "Pune"],
})
print(df.isna().sum())
df["age"] = df["age"].fillna(df["age"].median())
df["city"] = df["city"].fillna("Unknown")
print(df)Use vectorized calculations to find summary statistics, filter values and calculate z-scores.
import numpy as np
data = np.array([12, 15, 11, 18, 20, 14])
print("mean:", data.mean(), "std:", round(data.std(), 2))
print("above mean:", data[data > data.mean()])
z = (data - data.mean()) / data.std()
print(np.round(z, 2))Create a daily time series and smooth a metric with a three-day rolling average.
import pandas as pd
days = pd.date_range("2026-01-01", periods=10, freq="D")
df = pd.DataFrame({
"date": days,
"visits": [120, 135, 128, 150, 160, 155, 170, 165, 180, 175],
})
df["rolling_3d"] = df["visits"].rolling(3).mean().round(1)
print(df)A free, browser-based Python environment. You write Python, run it and see the output, with NumPy and Pandas available for data analysis.
Yes. It runs CPython 3.13.2 compiled to WebAssembly through Pyodide 0.28.3 inside your browser tab.
Yes. You can import NumPy and Pandas. The compiler downloads either package on demand when your code imports it.
No. Open the page and start running code. No account and no installation are needed.
No. Your code runs inside your browser tab. Analytics records only whether a Python tool run succeeds or fails, never your code or output.
The current editor supports Python's standard library plus NumPy and Pandas. It does not provide a control for installing additional packages.
The Python runtime and any imported data packages are downloaded into your browser when first needed. Later runs in the same session are faster.
It runs in the browser and cannot directly access files on your computer. Very heavy computations or large datasets may be slower or exceed browser memory, so it is best suited to learning, practice and quick experiments.
Yes. Start with the examples on this page, then solve the Pandas, NumPy and statistics problems on our Python Interview Questions page, which includes a live editor.
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