Working with tabular data through Dataframes#

Previously, we learned about Series: an ordered collection of observations, analogous to a numpy vector but with superpowers.

In this tutorial, we’ll learn about DataFrames, a method of holding tabular data in which each row is an observation, and each column is a variable. (OK, there are some different forms of tabular data, but that’s the most common format you’ll encounter).

To illustrate, here’s a small pandas DataFrame (created by importing data from a spreadsheet you can find here):

import pandas as pd

smallworld = pd.read_csv(
country region gdppcap08 polityIV
0 Brazil S. America 10296 18
1 Germany W. Europe 35613 20
2 Mexico N. America 14495 18
3 Mozambique Africa 855 16
4 Russia C&E Europe 16139 17
5 Ukraine C&E Europe 7271 16

As you can see, each of the 6 rows in the DataFrame world is a different country, and each column contains different information about that country—the country’s name, its region, its income level (GDP per Capita in 2008), and how close it was to an idealized liberal democracy in 2008 (it’s polity IV score).

What is a DataFrame?#

Where a Series was a one-dimensional collection of data, a DataFrame is fundamentally two-dimensional. As a result, it has many of the same types of features as a Series, just generalized to two dimensions. Here we show the breakdown of key aspects of a DataFrame that we’ll discuss throughout this lesson.

Index and Columns#

For example, like a Series, a DataFrame has an index that labels every row: in this case, it’s the usual default index that labels each row with its initial row number. Unlike a Series, however, DataFrames have a second set of labels: column names!

# Here are the row labels
# (Note that a "range index" is just
# another way of labeling each row with its row number)
RangeIndex(start=0, stop=6, step=1)
# And here is our column index.
# Note that while we don't call it "index",
# the column names are of type Index.
# They really are the same as row indices,
# just for columns

Index(['country', 'region', 'gdppcap08', 'polityIV'], dtype='object')

Constructing DataFrames#

As with Series, there are many ways to construct a DataFrame. Honestly, by far the most common is that you’ll read in a dataset from a file. Pandas offers lots of tools for doing this depending on the format of the data you’re importing. We’ll discuss this more in future lessons, but here are just a few methods to know about:

  • pd.read_csv: Read in a comma-separated-value file

  • pd.read_excel: Read in an Excel (.xls and .xlsx) spreadsheet

  • pd.read_stata: Read Stata (.dta) datasets

  • pd.read_hdf: Read HDF (.hdf) datasets

  • pd.read_sql: Read from a SQL database

Similarly, if we have an existing DataFrame (let’s call it df) we want to output to a file or database, we can use complimentary methods of df to do so such as:

  • df.to_csv: Write to a comma-separated-value file

  • df.to_excel: Write to an Excel (.xls and .xlsx) spreadsheet

  • df.to_stata: Write to a stata (.dta) dataset

  • df.to_hdf: Write to an HDF (.hdf) dataset

  • df.to_sql: Write to a SQL database

You can find a full list of IO methods here!

But you can also construct DataFrames by hand. The easiest (and most common) way is by passing in a Dictionary, where the keys will become column names and the values are column values:

df = pd.DataFrame(
        "animals": ["dog", "cat", "bird", "fish"],
        "can_swim": [True, False, False, True],
        "has_fur": [True, True, False, False],
animals can_swim has_fur
0 dog True True
1 cat False True
2 bird False False
3 fish True False

Getting To Know Your DataFrame#

While our toy smallworld dataset is small enough to easy print out and visualize, most datasets worth working with are too big to just look at. In those situations, we need tools to summarize the contents of our DataFrame.

Let’s load up a version of the smallworld dataset we looked at above that actually has all the countries in the world (instead of just 6). You can find the original dataset here.

world = pd.read_csv(
country region gdppcap08 polityIV
0 Albania C&E Europe 7715 17.8
1 Algeria Africa 8033 10.0
2 Angola Africa 5899 8.0
3 Argentina S. America 14333 18.0
4 Armenia C&E Europe 6070 15.0
... ... ... ... ...
140 Venezuela S. America 12804 16.0
141 Vietnam Asia-Pacific 2785 3.0
142 Yemen Middle East 2400 8.0
143 Zambia Africa 1356 15.0
144 Zimbabwe Africa 188 6.0

145 rows × 4 columns

As you can see, pandas prints out a bunch of the rows, but not all the rows (note the ... in the middle) in an effort to not take over your monitor. This DataFrame could theoretically be printed out in its entirety (as noted at the bottom of the output, it only has 145 rows), but in the real world, we often work with datasets with hundreds of thousands or millions of rows where printing just isn’t possible. So here are some methods for “getting to know your data”:

Let’s look at the first 5 rows using .head():

country region gdppcap08 polityIV
0 Albania C&E Europe 7715 17.8
1 Algeria Africa 8033 10.0
2 Angola Africa 5899 8.0
3 Argentina S. America 14333 18.0
4 Armenia C&E Europe 6070 15.0

And look at the last 5 rows with .tail():

country region gdppcap08 polityIV
140 Venezuela S. America 12804 16.0
141 Vietnam Asia-Pacific 2785 3.0
142 Yemen Middle East 2400 8.0
143 Zambia Africa 1356 15.0
144 Zimbabwe Africa 188 6.0

View a random subset of rows (here, 5). This is valuable because the first rows of a dataset aren’t always representative of the dataset. Often datasets are ordered (this dataset is sorted alphabetically by country), and seeing the first or last few entries can sometimes be misleading. Random sampling can reduce this effect.

country region gdppcap08 polityIV
99 Oman Middle East 22478 2.000000
23 Chad Africa 1455 8.000000
98 Norway Scandinavia 58138 20.000000
22 Central African Republic Africa 736 10.200000
108 Romania C&E Europe 14065 18.333333

We can use len() to get the number of rows:


Or len() and .columns to get the number of columns:


We can also check the data type of each column with .dtypes (note the s):

country       object
region        object
gdppcap08      int64
polityIV     float64
dtype: object

Or a slightly more nicely formatted summary with .info():
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 145 entries, 0 to 144
Data columns (total 4 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   country    145 non-null    object 
 1   region     145 non-null    object 
 2   gdppcap08  145 non-null    int64  
 3   polityIV   145 non-null    float64
dtypes: float64(1), int64(1), object(2)
memory usage: 4.7+ KB

We can also summary statistics for each numeric column (objects are ignored) with .describe():

gdppcap08 polityIV
count 145.000000 145.000000
mean 13251.993103 13.407816
std 14802.581676 6.587626
min 188.000000 0.000000
25% 2153.000000 7.666667
50% 7271.000000 16.000000
75% 19330.000000 19.000000
max 85868.000000 20.000000

Finally, we can list out all the column names. Note that when a table has a lot of columns, the output of .columns will be truncated. When that happens, this little hack will ensure you get to see all the columns:

for c in world.columns:

Subsetting a DataFrame#

As with Series, one of the most important skills for working with DataFrames is knowing how to subset them. Thankfully, subsetting DataFrames is just like subsetting Series but in two dimensions.


To subset a DataFrame using iloc, we now have to pass two arguments into iloc separated by a comma (the first entry subsets rows, the second columns, just like in numpy).

For example, if we wanted the entry in the fourth row of the first column, we would use:

world.iloc[3, 0]

And like with Series and numpy, iloc supports slices. Here are the first two rows of the first three columns:

world.iloc[0:2, 0:3]
country region gdppcap08
0 Albania C&E Europe 7715
1 Algeria Africa 8033

If you want to get a subset on one dimension, but all the entries on the other, just pass : for the dimension on which you want all the data (just like in numpy). Here are the first two rows and all the columns:

world.iloc[0:2, :]
country region gdppcap08 polityIV
0 Albania C&E Europe 7715 17.8
1 Algeria Africa 8033 10.0

If you ONLY pass one set of arguments, though, those will be applied to the first dimension (rows), just like in numpy. Thus .iloc[0:2] is the same as .iloc[0:2, :].

country region gdppcap08 polityIV
0 Albania C&E Europe 7715 17.8
1 Algeria Africa 8033 10.0


.loc generalized from Series to DataFrames using the same tricks as .iloc: if you pass two arguments, the first will subset rows (though for .loc, the subsetting is on index values, not row numbers), and the second will subset columns (again, on column names, not column order).

# Index value 1, column country
world.loc[1, "country"]

And just like in Series, if you pass a range to .loc, the end points will be included (unlike with most Python functions)

world.loc[0:1, "country"]
0    Albania
1    Algeria
Name: country, dtype: object

Finally, as with .iloc, if you pass a single argument to .loc, it will subset on the first dimension (rows):

country region gdppcap08 polityIV
0 Albania C&E Europe 7715 17.8
1 Algeria Africa 8033 10.0
2 Angola Africa 5899 8.0
3 Argentina S. America 14333 18.0

Logical Tests#

Subsetting with logical tests also works in a familiar manner for DataFrames:

  • If you pass a single boolean array to .loc, it will subset on rows.

  • If the Boolean array has an Index (i.e. if it’s a Series), then alignment will take place on index values.

  • If the Boolean array does NOT have an index (i.e. it’s a list of Booleans), then alignment will take place on row order.

  • To subset columns based on a test, you have to use .loc[:, YOUR_TEST_HERE].

To illustrate, let’s start by shuffling our DataFrame so that index values and row numbers aren’t the same:

world = world.sort_values("gdppcap08")
country region gdppcap08 polityIV
144 Zimbabwe Africa 188 6.0
29 Congo Kinshasa Africa 321 15.0
76 Liberia Africa 388 10.0
53 Guinea-Bissau Africa 538 11.0
40 Eritrea Africa 632 3.0
# Test with an index -> subset rows, align on index
relatively_democratic = world.loc[world["polityIV"] > 10]
country region gdppcap08 polityIV
29 Congo Kinshasa Africa 321 15.000000
53 Guinea-Bissau Africa 538 11.000000
96 Niger Africa 684 15.333333
22 Central African Republic Africa 736 10.200000
113 Sierra Leone Africa 766 15.000000

And if we want to subset columns on a Boolean (admittedly a silly example, but you get the idea):

relatively_democratic = relatively_democratic.loc[
    :, (world.columns == "country") | (world.columns == "gdppcap08")
country gdppcap08
29 Congo Kinshasa 321
53 Guinea-Bissau 538
96 Niger 684
22 Central African Republic 736
113 Sierra Leone 766
... ... ...
93 Netherlands 40849
124 Switzerland 42536
62 Ireland 44200
137 United States 46716
98 Norway 58138

96 rows × 2 columns

A Reminder on Combining Logical Tests#

As with numpy, when combining logical tests, you have to wrap each test in parentheses. If you don’t, and just pass world.columns == "country" | world.columns == "gdppcap08", you will get a very confusing Error:

    :, world.columns == "country" | world.columns == "gdppcap08"

TypeError                                 Traceback (most recent call last)
File ~/opt/miniconda3/lib/python3.10/site-packages/pandas/core/ops/, in na_logical_op(x, y, op)
    302 try:
    303     # For exposition, write:
    304     #  yarr = isinstance(y, np.ndarray)
    309     # Then Cases where this goes through without raising include:
    310     #  (xint or xbool) and (yint or bool)
--> 311     result = op(x, y)
    312 except TypeError:

File ~/opt/miniconda3/lib/python3.10/site-packages/pandas/core/, in ror_(left, right)
     57 def ror_(left, right):
---> 58     return operator.or_(right, left)

TypeError: unsupported operand type(s) for |: 'str' and 'str'


    339 return result.reshape(x.shape)

TypeError: Cannot perform 'ror_' with a dtyped [object] array and scalar of type [bool]

[] Square brackets#

As with Series, single square brackets in pandas change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is nothing that one can do with square brackets that you can’t do with .loc and .iloc, so if they seem to strange, you don’t have to use them.

With that said, as summarized below, [] is actually much safer on DataFrames than on Series.

The rules of [] in DataFrames are:

  • If your entry is a single column name, or a list of column names, it will return those columns.

  • If your entry is a slice, it will work like iloc and select rows based on row order.

  • If your entry is a Boolean array, and of exactly the same length as the number of rows in your data, it will subset rows.

    • Note this means that [] does not do the same thing we saw .loc do above where, if passed a short Boolean array, it will assume any row without an entry in the Boolean array should be dropped.

# Select one column
144          Zimbabwe
29     Congo Kinshasa
76            Liberia
53      Guinea-Bissau
40            Eritrea
Name: country, dtype: object
# Select multiple columns
world[["country", "gdppcap08"]].head()
country gdppcap08
144 Zimbabwe 188
29 Congo Kinshasa 321
76 Liberia 388
53 Guinea-Bissau 538
40 Eritrea 632
# Boolean test
world[world["gdppcap08"] > 10000].head()
country region gdppcap08 polityIV
79 Macedonia C&E Europe 10041 19.0
118 South Africa Africa 10109 19.0
16 Brazil S. America 10296 18.0
30 Costa Rica S. America 11241 20.0
68 Kazakhstan C&E Europe 11315 4.0
# Slice of rows
country region gdppcap08 polityIV
144 Zimbabwe Africa 188 6.0
29 Congo Kinshasa Africa 321 15.0
76 Liberia Africa 388 10.0

DataFrames: Collections of Series#

While it is natural to think of a DataFrame as a single table (like a numpy matrix), in reality, a DataFrame is just a collection of Series.

To see this, let’s pull out an individual column using square bracket notation, and check its type:



And that means that you can always pull out a column from a DataFrame and manipulate it using the tools you’ve already learned from the Series tutorial. And because you know how to extract the numpy array that underlies a Series, that means you also always know how to move from DataFrames to numpy arrays if you need to:

# Get numpy array under one column

Selecting Series versus Selecting DataFrames#

There is one point of nuance worth exploring: when you extract a single column from a DataFrame, you have the choice of either extracting a Series, or extracting a DataFrame with a single column. What determines this is whether you use one pair of square brackets, or two.

If you use a single set of square brackets (or pass just the name of a column to loc, you get back a Series. But if you pass a list with the column name, you get back a DataFrame:


This also holds for rows, by the way. If you ask for a single row, you will actually get back a (newly construted) Series:


(Obviously, if you ask for more than one row, or more than one column, you will always get back a DataFrame, since the object you’re requesting is intrinsically 2-dimensional and can’t be represented as a Series. )


We have explored how DataFrames are versatile tools for loading in (and writing out) data of diverse file types and for selecting subsets of those data (filtering) for further analysis. These techniques are at the core of how we typically work with tabular datasets in data science. Int he exercises that follow, you’ll have a chance to get experience using those tools. And in the next week, we will dive deeply into how to work with DataFrames and use them to ready data for further analysis.