Pandas Analyzing Data
Before cleaning or transforming a DataFrame, use head(), tail(), info(), and describe() to quickly understand its shape, types, and distribution.
First Look: head() and tail()
Real-world datasets can have thousands or millions of rows, so printing the whole DataFrame is rarely useful. pandas gives you head() and tail() to preview the beginning and end of a DataFrame so you can confirm it loaded correctly and get a feel for what the columns contain before you do anything else.
Example
import pandas as pd
data = {
'WorkoutMinutes': [30, 45, 60, 60, 45, 60],
'HeartRate': [109, 117, 110, 104, 117, 98],
'MaxHeartRate': [175, 148, 130, 132, 148, 124],
'CaloriesBurned': [409.1, 479.0, 340.0, 320.8, 320.0, 300.0]
}
df = pd.DataFrame(data)
print(df.head(3)) # first 3 rows
print(df.tail(2)) # last 2 rows- head(n) returns the first n rows and defaults to 5 if you omit n
- tail(n) returns the last n rows and also defaults to 5
- Both return a new DataFrame, so you can chain methods after them, e.g. df.head().describe()
- A quick head()/tail() check will reveal shifted columns, wrong headers, or an unexpected sort order
Digging Deeper with info()
head() and tail() show you values, but info() shows you structure. It reports the number of rows, the column names in order, how many non-null values each column has, and the data type pandas inferred for each column — all in a single call.
Example
import pandas as pd
import numpy as np
data = {
'WorkoutMinutes': [30, 45, 60, 60, 45, 60],
'HeartRate': [109, 117, 110, 104, 117, 98],
'MaxHeartRate': [175, 148, 130, 132, 148, 124],
'CaloriesBurned': [409.1, 479.0, np.nan, 320.8, 320.0, 300.0]
}
df = pd.DataFrame(data)
df.info()
# RangeIndex: 6 entries, 0 to 5
# Data columns (total 4 columns):
# # Column Non-Null Count Dtype
# --- ------ -------------- -----
# 0 WorkoutMinutes 6 non-null int64
# 1 HeartRate 6 non-null int64
# 2 MaxHeartRate 6 non-null int64
# 3 CaloriesBurned 5 non-null float64Summary Statistics with describe()
describe() computes summary statistics for every numeric column in one call: count, mean, standard deviation, minimum, maximum, and the 25th, 50th, and 75th percentiles. It is the fastest way to sanity-check whether your numbers look reasonable before you build charts or models on top of them.
Example
import pandas as pd
data = {
'WorkoutMinutes': [30, 45, 60, 60, 45, 60],
'HeartRate': [109, 117, 110, 104, 117, 98],
'MaxHeartRate': [175, 148, 130, 132, 148, 124],
'CaloriesBurned': [409.1, 479.0, 340.0, 320.8, 320.0, 300.0]
}
df = pd.DataFrame(data)
print(df.describe())
# Prints count, mean, std, min, 25%, 50%, 75%, and max
# for every numeric column, e.g. df.describe()['CaloriesBurned']['mean']Exercise: Pandas Analyzing Data
By default, how many rows does head() display if no argument is given?
