• How do I learn a new Python package?

  • How can I use pandas dataframes in my research?


  • Learn simple and some more advanced usage of pandas dataframes

  • Get a feeling for when pandas is useful and know where to find more information

Pandas is a Python package that provides high-performance and easy to use data structures and data analysis tools. This page provides a brief overview of pandas, but the open source community developing the pandas package has also created excellent documentation and training material, including:

Let’s get a flavor of what we can do with pandas. We will be working with an example dataset containing the passenger list from the Titanic, which is often used in Kaggle competitions and data science tutorials. First step is to load pandas:

import pandas as pd

We can download the data from this GitHub repository by visiting the page and saving it to disk, or by directly reading into a dataframe:

url = ""
titanic = pd.read_csv(url)

We can now view the dataframe to get an idea of what it contains and print some summary statistics of its numerical data:

# print the first 5 lines of the dataframe

# print summary statistics for each column

Ok, so we have information on passenger names, survival (0 or 1), age, ticket fare, number of siblings/spouses, etc. With the summary statistics we see that the average age is 29.7 years, maximum ticket price is 512 USD, 38% of passengers survived, etc.

Let’s say we’re interested in the survival probability of different age groups. With two one-liners, we can find the average age of those who survived or didn’t survive, and plot corresponding histograms of the age distribution:

titanic.hist(column='Age', by='Survived', bins=25, figsize=(8,10),
             layout=(2,1), zorder=2, sharex=True, rwidth=0.9);

Clearly, pandas dataframes allows us to do advanced analysis with very few commands, but it takes a while to get used to how dataframes work so let’s get back to basics.

Getting help

Series and DataFrames have a lot functionality, but how can we find out what methods are available and how they work? One way is to visit the API reference and reading through the list. Another way is to use the autocompletion feature in Jupyter and type e.g. titanic["Age"]. in a notebook and then hit TAB twice - this should open up a list menu of available methods and attributes.

Jupyter also offers quick access to help pages (docstrings) which can be more efficient than searching the internet. Two ways exist:

  • Write a function name followed by question mark and execute the cell, e.g. write titanic.hist? and hit SHIFT + ENTER.

  • Write the function name and hit SHIFT + TAB.

What’s in a dataframe?

As we saw above, pandas dataframes are a powerful tool for working with tabular data. A pandas DataFrame object is composed of rows and columns:


Each column of a dataframe is a series object - a dataframe is thus a collection of series. Let’s inspect one column of the Titanic passanger list data (first downloading and reading the titanic.csv datafile into a dataframe if needed, see above):

titanic.Age          # same as above

The columns, rows and dtypes can be listed through corresponding attributes:


We saw above how to select a single column, but there are other ways of selecting (and setting) single or multiple rows, columns and values:[0,"Age"]            # select single value by row and column *name* (fast)[0,"Age"] = 42       # set single value by row and column *name* (fast)
titanic.iat[0,5]               # select same value by row and column *number* (fast)
titanic.loc[0:2, "Name":"Age"] # slice the dataframe by row and column *names*
titanic.iloc[0:2,3:6]          # same slice as above by row and column *numbers*
titanic["foo"] = "bar"         # set a whole column

Dataframes also support boolean indexing, just like we saw for numpy arrays:

titanic[titanic["Age"] > 70]
# ".str" creates a string object from a column

What if your dataset has missing data? Pandas uses the value np.nan to represent missing data, and by default does not include it in any computations. We can find missing values, drop them from our dataframe, replace them with any value we like or do forward or backward filling:

titanic.isna()                    # returns boolean mask of NaN values
titanic.dropna()                  # drop missing values
titanic.dropna(how="any")         # or how="all"
titanic.dropna(subset=["Cabin"])  # only drop NaNs from one column
titanic.fillna(0)                 # replace NaNs with zero
titanic.fillna(method='ffill')    # forward-fill NaNs

Exploring dataframes

  • Have a look at the available methods and attributes using the API reference or the autocomplete feature in Jupyter.

  • Try out a few methods using the Titanic dataset and have a look at the docstrings (help pages) of methods that pique your interest

  • Compute the mean age of the first 10 passengers by slicing and the mean method

  • (Advanced) Using boolean indexing, compute the survival rate (mean of “Survived” values) among passengers over and under the average age.

Tidy data

The above analysis was rather straightforward thanks to the fact that the dataset is tidy.


In short, columns should be variables and rows should be measurements, and adding measurements (rows) should then not require any changes to code that reads the data.

What would untidy data look like? Here’s an example from some run time statistics from a 1500 m running event:

df = pd.DataFrame([
        {'Runner': 'Runner 1', 400: 64, 800: 128, 1200: 192, 1500: 240},
        {'Runner': 'Runner 2', 400: 80, 800: 160, 1200: 240, 1500: 300},
        {'Runner': 'Runner 3', 400: 96, 800: 192, 1200: 288, 1500: 360},

To make untidy data tidy, a common operation is to “melt” it, which is to convert it from wide form to a long form:

df = pd.melt(df, id_vars="Runner",
             value_vars=[400, 800, 1200, 1500],

In this form it’s easier to filter, group, join and aggregate the data, and it’s also easier to model relationships between variables.

The opposite of melting is to pivot data, which can be useful to view data in different ways as we’ll see below.

For a detailed exposition of data tidying, have a look at this article.

Working with dataframes

We saw above how we can read in data into a dataframe using the read_csv method. Pandas also understands multiple other formats, for example using read_excel, read_hdf, read_json, etc. (and corresponding methods to write to file: to_csv, to_excel, to_hdf, to_json, etc.)

But sometimes you would want to create a dataframe from scratch. Also this can be done in multiple ways, for example starting with a numpy array:

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))

or a dictionary:

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
                   'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
                   'C': np.array([3] * 8, dtype='int32'),
                   'D': np.random.randn(8),
                   'E': np.random.randn(8)})

There are many ways to operate on dataframes. Let’s look at a few examples in order to get a feeling of what’s possible and what the use cases can be.

We can easily split and concatenate or append dataframes:

sub1, sub2, sub3 = df[:2], df[2:4], df[4:]
pd.concat([sub1, sub2, sub3])
sub1.append([sub2, sub3])      # same as above

Dataframes can also be merged similarly to in SQL:

m1 = df.loc[:3, "A":"B"]
m2 = df.loc[3:6, ["A", "D", "E"]]
# merge two dataframes on column "A"
pd.merge(m1, m2, on="A")

In fact, much of what can be done in SQL is also possible with pandas.

Functions can be applied to a whole dataframe or parts of it:

df.apply(np.cumsum)   # you can also pass your own custom functions
df.loc[:, "C":"E"].apply(np.cumsum)

Most common statistical functions are in fact already available as dataframe methods, like std(), min(), max(), cumsum(), median(), skew(), var() etc.

pivot_table() and groupby() are two powerful methods which are applied to dataframes to split and aggregate data in groups. They work similarly but differ in the shape of the result. To see what’s possible, let’s return to the Titanic dataset. We start by rounding all ages to the nearest decade and then create a pivot table showing the mean of fares split by gender and survival:

titanic["Age"] = titanic["Age"].round(-1)
pd.pivot_table(titanic, values="Fare", index=["Sex", "Survived"],
               columns=["Age"], aggfunc=np.mean)

The same operation with group-by is:

titanic.groupby(["Sex", "Survived", "Age"])["Fare"].mean()

Analyze the Titanic passenger list dataset

In the Titanic passenger list dataset, investigate the family size of the passengers (i.e. the “SibSp” column).

  • What different family sizes exist in the passenger list? Hint: try the unique method

  • What are the names of the people in the largest family group?

  • (Advanced) Create histograms showing the distribution of family sizes for passengers split by the fare, i.e. one group of high-fare passengers (where the fare is above average) and one for low-fare passengers (Hint: you can use the lambda function lambda x: "Poor" if df["Fare"].loc[x] < df["Fare"].mean() else "Rich")

Time series superpowers

An introduction of pandas wouldn’t be complete without mention of its special abilities to handle time series. To show just a few examples, we will use a new dataset of Nobel prize laureates:

nobel = pd.read_csv("")

This dataset has three columns for time, “born”/”died” and “year”. These are represented as strings and integers, respectively, and need to be converted to datetime format:

# the errors='coerce' argument is needed because the dataset is a bit messy
nobel["born"] = pd.to_datetime(nobel["born"], errors ='coerce')
nobel["died"] = pd.to_datetime(nobel["died"], errors ='coerce')
nobel["year"] = pd.to_datetime(nobel["year"], format="%Y")

Pandas knows a lot about dates:


We can add a column containing the (approximate) lifespan in years rounded to one decimal:

nobel["lifespan"] = round((nobel["died"] - nobel["born"]).dt.days / 365, 1)

and then plot a histogram of lifespans:

nobel.hist(column='lifespan', bins=25, figsize=(8,10), rwidth=0.9)

Finally, let’s see one more example of an informative plot produced by a single line of code:

nobel.boxplot(column="lifespan", by="category")

Analyze the Nobel prize dataset

  • What country has received the largest number of Nobel prizes, and how many? How many countries are represented in the dataset? Hint: use the describe() method on the bornCountryCode column.

  • Create a histogram of the age when the laureates received their Nobel prizes. Hint: follow the above steps we performed for the lifespan.

  • List all the Nobel laureates from your country.

Now more advanced steps:

  • First add a column “number” to the nobel dataframe containing 1’s (to enable the counting below).

  • Now define an array of 4 countries of your choice and extract only laureates from these countries:

    countries = np.array([COUNTRY1, COUNTRY2, COUNTRY3, COUNTRY4])
    subset = nobel.loc[nobel['bornCountry'].isin(countries)]
  • Create a pivot table to view a spreadsheet like structure, and view it:

    table = subset.pivot_table(values="number", index="bornCountry", columns="category", aggfunc=np.sum)
  • (Optional) Install the seaborn visualization library if you don’t already have it, and create a heatmap of your table:

    import seaborn as sns
  • Play around with other nice looking plots:

    sns.violinplot(y="year", x="bornCountry",inner="stick", data=subset);
    sns.swarmplot(y="year", x="bornCountry", data=subset, alpha=.5);
    subset_physchem = nobel.loc[nobel['bornCountry'].isin(countries) & (nobel['category'].isin(['physics']) | nobel['category'].isin(['chemistry']))]
    sns.catplot(x="bornCountry", y="year", col="category", data=subset_physchem, kind="swarm");
    sns.catplot(x="bornCountry", col="category", data=subset_physchem, kind="count");


  • pandas dataframes are a good data structure for tabular data

  • Dataframes allow both simple and advanced analysis in very compact form