Tools - pandas

The pandas library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the DataFrame, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.

Prerequisites:

  • NumPy – if you are not familiar with NumPy, we recommend that you go through the NumPy tutorial now.

Setup

First, let's import pandas. People usually import it as pd:

In [1]:
import pandas as pd

Series objects

The pandas library contains these useful data structures:

  • Series objects, that we will discuss now. A Series object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).
  • DataFrame objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).
  • Panel objects. You can see a Panel as a dictionary of DataFrames. These are less used, so we will not discuss them here.

Creating a Series

Let's start by creating our first Series object!

In [2]:
s = pd.Series([2,-1,3,5])
s
Out[2]:
0    2
1   -1
2    3
3    5
dtype: int64

Similar to a 1D ndarray

Series objects behave much like one-dimensional NumPy ndarrays, and you can often pass them as parameters to NumPy functions:

In [3]:
import numpy as np
np.exp(s)
Out[3]:
0      7.389056
1      0.367879
2     20.085537
3    148.413159
dtype: float64

Arithmetic operations on Series are also possible, and they apply elementwise, just like for ndarrays:

In [4]:
s + [1000,2000,3000,4000]
Out[4]:
0    1002
1    1999
2    3003
3    4005
dtype: int64

Similar to NumPy, if you add a single number to a Series, that number is added to all items in the Series. This is called broadcasting:

In [5]:
s + 1000
Out[5]:
0    1002
1     999
2    1003
3    1005
dtype: int64

The same is true for all binary operations such as * or /, and even conditional operations:

In [6]:
s < 0
Out[6]:
0    False
1     True
2    False
3    False
dtype: bool

Index labels

Each item in a Series object has a unique identifier called the index label. By default, it is simply the rank of the item in the Series (starting at 0) but you can also set the index labels manually:

In [7]:
s2 = pd.Series([68, 83, 112, 68], index=["alice", "bob", "charles", "darwin"])
s2
Out[7]:
alice       68
bob         83
charles    112
darwin      68
dtype: int64

You can then use the Series just like a dict:

In [8]:
s2["bob"]
Out[8]:
83

You can still access the items by integer location, like in a regular array:

In [9]:
s2[1]
Out[9]:
83

To make it clear when you are accessing by label or by integer location, it is recommended to always use the loc attribute when accessing by label, and the iloc attribute when accessing by integer location:

In [10]:
s2.loc["bob"]
Out[10]:
83
In [11]:
s2.iloc[1]
Out[11]:
83

Slicing a Series also slices the index labels:

In [12]:
s2.iloc[1:3]
Out[12]:
bob         83
charles    112
dtype: int64

This can lead to unexpected results when using the default numeric labels, so be careful:

In [13]:
surprise = pd.Series([1000, 1001, 1002, 1003])
surprise
Out[13]:
0    1000
1    1001
2    1002
3    1003
dtype: int64
In [14]:
surprise_slice = surprise[2:]
surprise_slice
Out[14]:
2    1002
3    1003
dtype: int64

Oh look! The first element has index label 2. The element with index label 0 is absent from the slice:

In [15]:
try:
    surprise_slice[0]
except KeyError as e:
    print("Key error:", e)
Key error: 0

But remember that you can access elements by integer location using the iloc attribute. This illustrates another reason why it's always better to use loc and iloc to access Series objects:

In [16]:
surprise_slice.iloc[0]
Out[16]:
1002

Init from dict

You can create a Series object from a dict. The keys will be used as index labels:

In [17]:
weights = {"alice": 68, "bob": 83, "colin": 86, "darwin": 68}
s3 = pd.Series(weights)
s3
Out[17]:
alice     68
bob       83
colin     86
darwin    68
dtype: int64

You can control which elements you want to include in the Series and in what order by explicitly specifying the desired index:

In [18]:
s4 = pd.Series(weights, index = ["colin", "alice"])
s4
Out[18]:
colin    86
alice    68
dtype: int64

Automatic alignment

When an operation involves multiple Series objects, pandas automatically aligns items by matching index labels.

In [19]:
print(s2.keys())
print(s3.keys())

s2 + s3
Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')
Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')
Out[19]:
alice      136.0
bob        166.0
charles      NaN
colin        NaN
darwin     136.0
dtype: float64

The resulting Series contains the union of index labels from s2 and s3. Since "colin" is missing from s2 and "charles" is missing from s3, these items have a NaN result value. (ie. Not-a-Number means missing).

Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:

In [20]:
s5 = pd.Series([1000,1000,1000,1000])
print("s2 =", s2.values)
print("s5 =", s5.values)

s2 + s5
s2 = [ 68  83 112  68]
s5 = [1000 1000 1000 1000]
Out[20]:
alice     NaN
bob       NaN
charles   NaN
darwin    NaN
0         NaN
1         NaN
2         NaN
3         NaN
dtype: float64

Pandas could not align the Series, since their labels do not match at all, hence the full NaN result.

Init with a scalar

You can also initialize a Series object using a scalar and a list of index labels: all items will be set to the scalar.

In [21]:
meaning = pd.Series(42, ["life", "universe", "everything"])
meaning
Out[21]:
life          42
universe      42
everything    42
dtype: int64

Series name

A Series can have a name:

In [22]:
s6 = pd.Series([83, 68], index=["bob", "alice"], name="weights")
s6
Out[22]:
bob      83
alice    68
Name: weights, dtype: int64

Plotting a Series

Pandas makes it easy to plot Series data using matplotlib (for more details on matplotlib, check out the matplotlib tutorial). Just import matplotlib and call the plot() method:

In [23]:
%matplotlib inline
import matplotlib.pyplot as plt
temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]
s7 = pd.Series(temperatures, name="Temperature")
s7.plot()
plt.show()

There are many options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent Visualization section of pandas' documentation, and look at the example code.

Handling time

Many datasets have timestamps, and pandas is awesome at manipulating such data:

  • it can represent periods (such as 2016Q3) and frequencies (such as "monthly"),
  • it can convert periods to actual timestamps, and vice versa,
  • it can resample data and aggregate values any way you like,
  • it can handle timezones.

Time range

Let's start by creating a time series using pd.date_range(). This returns a DatetimeIndex containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm.

In [24]:
dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')
dates
Out[24]:
DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',
               '2016-10-29 19:30:00', '2016-10-29 20:30:00',
               '2016-10-29 21:30:00', '2016-10-29 22:30:00',
               '2016-10-29 23:30:00', '2016-10-30 00:30:00',
               '2016-10-30 01:30:00', '2016-10-30 02:30:00',
               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],
              dtype='datetime64[ns]', freq='H')

This DatetimeIndex may be used as an index in a Series:

In [25]:
temp_series = pd.Series(temperatures, dates)
temp_series
Out[25]:
2016-10-29 17:30:00    4.4
2016-10-29 18:30:00    5.1
2016-10-29 19:30:00    6.1
2016-10-29 20:30:00    6.2
2016-10-29 21:30:00    6.1
2016-10-29 22:30:00    6.1
2016-10-29 23:30:00    5.7
2016-10-30 00:30:00    5.2
2016-10-30 01:30:00    4.7
2016-10-30 02:30:00    4.1
2016-10-30 03:30:00    3.9
2016-10-30 04:30:00    3.5
Freq: H, dtype: float64

Let's plot this series:

In [26]:
temp_series.plot(kind="bar")

plt.grid(True)
plt.show()

Resampling

Pandas lets us resample a time series very simply. Just call the resample() method and specify a new frequency:

In [27]:
temp_series_freq_2H = temp_series.resample("2H")
temp_series_freq_2H
Out[27]:
DatetimeIndexResampler [freq=<2 * Hours>, axis=0, closed=left, label=left, convention=start, base=0]

The resampling operation is actually a deferred operation, which is why we did not get a Series object, but a DatetimeIndexResampler object instead. To actually perform the resampling operation, we can simply call the mean() method: Pandas will compute the mean of every pair of consecutive hours:

In [28]:
temp_series_freq_2H = temp_series_freq_2H.mean()

Let's plot the result:

In [29]:
temp_series_freq_2H.plot(kind="bar")
plt.show()

Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of 5.1 at 6:30pm, and 6.1 at 7:30pm. After resampling, we just have one value of 5.6, which is the mean of 5.1 and 6.1. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:

In [30]:
temp_series_freq_2H = temp_series.resample("2H").min()
temp_series_freq_2H
Out[30]:
2016-10-29 16:00:00    4.4
2016-10-29 18:00:00    5.1
2016-10-29 20:00:00    6.1
2016-10-29 22:00:00    5.7
2016-10-30 00:00:00    4.7
2016-10-30 02:00:00    3.9
2016-10-30 04:00:00    3.5
Freq: 2H, dtype: float64

Or, equivalently, we could use the apply() method instead:

In [31]:
temp_series_freq_2H = temp_series.resample("2H").apply(np.min)
temp_series_freq_2H
Out[31]:
2016-10-29 16:00:00    4.4
2016-10-29 18:00:00    5.1
2016-10-29 20:00:00    6.1
2016-10-29 22:00:00    5.7
2016-10-30 00:00:00    4.7
2016-10-30 02:00:00    3.9
2016-10-30 04:00:00    3.5
Freq: 2H, dtype: float64

Upsampling and interpolation

This was an example of downsampling. We can also upsample (ie. increase the frequency), but this creates holes in our data:

In [32]:
temp_series_freq_15min = temp_series.resample("15Min").mean()
temp_series_freq_15min.head(n=10) # `head` displays the top n values
Out[32]:
2016-10-29 17:30:00    4.4
2016-10-29 17:45:00    NaN
2016-10-29 18:00:00    NaN
2016-10-29 18:15:00    NaN
2016-10-29 18:30:00    5.1
2016-10-29 18:45:00    NaN
2016-10-29 19:00:00    NaN
2016-10-29 19:15:00    NaN
2016-10-29 19:30:00    6.1
2016-10-29 19:45:00    NaN
Freq: 15T, dtype: float64

One solution is to fill the gaps by interpolating. We just call the interpolate() method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:

In [33]:
temp_series_freq_15min = temp_series.resample("15Min").interpolate(method="cubic")
temp_series_freq_15min.head(n=10)
Out[33]:
2016-10-29 17:30:00    4.400000
2016-10-29 17:45:00    4.452911
2016-10-29 18:00:00    4.605113
2016-10-29 18:15:00    4.829758
2016-10-29 18:30:00    5.100000
2016-10-29 18:45:00    5.388992
2016-10-29 19:00:00    5.669887
2016-10-29 19:15:00    5.915839
2016-10-29 19:30:00    6.100000
2016-10-29 19:45:00    6.203621
Freq: 15T, dtype: float64
In [34]:
temp_series.plot(label="Period: 1 hour")
temp_series_freq_15min.plot(label="Period: 15 minutes")
plt.legend()
plt.show()

Timezones

By default datetimes are naive: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone aware by calling the tz_localize() method:

In [35]:
temp_series_ny = temp_series.tz_localize("America/New_York")
temp_series_ny
Out[35]:
2016-10-29 17:30:00-04:00    4.4
2016-10-29 18:30:00-04:00    5.1
2016-10-29 19:30:00-04:00    6.1
2016-10-29 20:30:00-04:00    6.2
2016-10-29 21:30:00-04:00    6.1
2016-10-29 22:30:00-04:00    6.1
2016-10-29 23:30:00-04:00    5.7
2016-10-30 00:30:00-04:00    5.2
2016-10-30 01:30:00-04:00    4.7
2016-10-30 02:30:00-04:00    4.1
2016-10-30 03:30:00-04:00    3.9
2016-10-30 04:30:00-04:00    3.5
Freq: H, dtype: float64

Note that -04:00 is now appended to all the datetimes. This means that these datetimes refer to UTC - 4 hours.

We can convert these datetimes to Paris time like this:

In [36]:
temp_series_paris = temp_series_ny.tz_convert("Europe/Paris")
temp_series_paris
Out[36]:
2016-10-29 23:30:00+02:00    4.4
2016-10-30 00:30:00+02:00    5.1
2016-10-30 01:30:00+02:00    6.1
2016-10-30 02:30:00+02:00    6.2
2016-10-30 02:30:00+01:00    6.1
2016-10-30 03:30:00+01:00    6.1
2016-10-30 04:30:00+01:00    5.7
2016-10-30 05:30:00+01:00    5.2
2016-10-30 06:30:00+01:00    4.7
2016-10-30 07:30:00+01:00    4.1
2016-10-30 08:30:00+01:00    3.9
2016-10-30 09:30:00+01:00    3.5
Freq: H, dtype: float64

You may have noticed that the UTC offset changes from +02:00 to +01:00: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):

In [37]:
temp_series_paris_naive = temp_series_paris.tz_localize(None)
temp_series_paris_naive
Out[37]:
2016-10-29 23:30:00    4.4
2016-10-30 00:30:00    5.1
2016-10-30 01:30:00    6.1
2016-10-30 02:30:00    6.2
2016-10-30 02:30:00    6.1
2016-10-30 03:30:00    6.1
2016-10-30 04:30:00    5.7
2016-10-30 05:30:00    5.2
2016-10-30 06:30:00    4.7
2016-10-30 07:30:00    4.1
2016-10-30 08:30:00    3.9
2016-10-30 09:30:00    3.5
Freq: H, dtype: float64

Now 02:30 is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:

In [38]:
try:
    temp_series_paris_naive.tz_localize("Europe/Paris")
except Exception as e:
    print(type(e))
    print(e)
<class 'pytz.exceptions.AmbiguousTimeError'>
Cannot infer dst time from Timestamp('2016-10-30 02:30:00'), try using the 'ambiguous' argument

Fortunately using the ambiguous argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:

In [39]:
temp_series_paris_naive.tz_localize("Europe/Paris", ambiguous="infer")
Out[39]:
2016-10-29 23:30:00+02:00    4.4
2016-10-30 00:30:00+02:00    5.1
2016-10-30 01:30:00+02:00    6.1
2016-10-30 02:30:00+02:00    6.2
2016-10-30 02:30:00+01:00    6.1
2016-10-30 03:30:00+01:00    6.1
2016-10-30 04:30:00+01:00    5.7
2016-10-30 05:30:00+01:00    5.2
2016-10-30 06:30:00+01:00    4.7
2016-10-30 07:30:00+01:00    4.1
2016-10-30 08:30:00+01:00    3.9
2016-10-30 09:30:00+01:00    3.5
Freq: H, dtype: float64

Periods

The pd.period_range() function returns a PeriodIndex instead of a DatetimeIndex. For example, let's get all quarters in 2016 and 2017:

In [40]:
quarters = pd.period_range('2016Q1', periods=8, freq='Q')
quarters
Out[40]:
PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',
             '2017Q3', '2017Q4'],
            dtype='period[Q-DEC]', freq='Q-DEC')

Adding a number N to a PeriodIndex shifts the periods by N times the PeriodIndex's frequency:

In [41]:
quarters + 3
Out[41]:
PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',
             '2018Q2', '2018Q3'],
            dtype='period[Q-DEC]', freq='Q-DEC')

The asfreq() method lets us change the frequency of the PeriodIndex. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):

In [42]:
quarters.asfreq("M")
Out[42]:
PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',
             '2017-09', '2017-12'],
            dtype='period[M]', freq='M')

By default, the asfreq zooms on the end of each period. We can tell it to zoom on the start of each period instead:

In [43]:
quarters.asfreq("M", how="start")
Out[43]:
PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',
             '2017-07', '2017-10'],
            dtype='period[M]', freq='M')

And we can zoom out:

In [44]:
quarters.asfreq("A")
Out[44]:
PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')

Of course we can create a Series with a PeriodIndex:

In [45]:
quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)
quarterly_revenue
Out[45]:
2016Q1    300
2016Q2    320
2016Q3    290
2016Q4    390
2017Q1    320
2017Q2    360
2017Q3    310
2017Q4    410
Freq: Q-DEC, dtype: int64
In [46]:
quarterly_revenue.plot(kind="line")
plt.show()

We can convert periods to timestamps by calling to_timestamp. By default this will give us the first day of each period, but by setting how and freq, we can get the last hour of each period:

In [47]:
last_hours = quarterly_revenue.to_timestamp(how="end", freq="H")
last_hours
Out[47]:
2016-03-31 23:00:00    300
2016-06-30 23:00:00    320
2016-09-30 23:00:00    290
2016-12-31 23:00:00    390
2017-03-31 23:00:00    320
2017-06-30 23:00:00    360
2017-09-30 23:00:00    310
2017-12-31 23:00:00    410
Freq: Q-DEC, dtype: int64

And back to periods by calling to_period:

In [48]:
last_hours.to_period()
Out[48]:
2016Q1    300
2016Q2    320
2016Q3    290
2016Q4    390
2017Q1    320
2017Q2    360
2017Q3    310
2017Q4    410
Freq: Q-DEC, dtype: int64

Pandas also provides many other time-related functions that we recommend you check out in the documentation. To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:

In [49]:
months_2016 = pd.period_range("2016", periods=12, freq="M")
one_day_after_last_days = months_2016.asfreq("D") + 1
last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()
last_bdays.to_period("H") + 9
Out[49]:
PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',
             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',
             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',
             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],
            dtype='period[H]', freq='H')

DataFrame objects

A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see DataFrames as dictionaries of Series.

Creating a DataFrame

You can create a DataFrame by passing a dictionary of Series objects:

In [50]:
people_dict = {
    "weight": pd.Series([68, 83, 112], index=["alice", "bob", "charles"]),
    "birthyear": pd.Series([1984, 1985, 1992], index=["bob", "alice", "charles"], name="year"),
    "children": pd.Series([0, 3], index=["charles", "bob"]),
    "hobby": pd.Series(["Biking", "Dancing"], index=["alice", "bob"]),
}
people = pd.DataFrame(people_dict)
people
Out[50]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112

A few things to note:

  • the Series were automatically aligned based on their index,
  • missing values are represented as NaN,
  • Series names are ignored (the name "year" was dropped),
  • DataFrames are displayed nicely in Jupyter notebooks, woohoo!

You can access columns pretty much as you would expect. They are returned as Series objects:

In [51]:
people["birthyear"]
Out[51]:
alice      1985
bob        1984
charles    1992
Name: birthyear, dtype: int64

You can also get multiple columns at once:

In [52]:
people[["birthyear", "hobby"]]
Out[52]:
birthyear hobby
alice 1985 Biking
bob 1984 Dancing
charles 1992 NaN

If you pass a list of columns and/or index row labels to the DataFrame constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:

In [53]:
d2 = pd.DataFrame(
        people_dict,
        columns=["birthyear", "weight", "height"],
        index=["bob", "alice", "eugene"]
     )
d2
Out[53]:
birthyear weight height
bob 1984.0 83.0 NaN
alice 1985.0 68.0 NaN
eugene NaN NaN NaN

Another convenient way to create a DataFrame is to pass all the values to the constructor as an ndarray, or a list of lists, and specify the column names and row index labels separately:

In [54]:
values = [
            [1985, np.nan, "Biking",   68],
            [1984, 3,      "Dancing",  83],
            [1992, 0,      np.nan,    112]
         ]
d3 = pd.DataFrame(
        values,
        columns=["birthyear", "children", "hobby", "weight"],
        index=["alice", "bob", "charles"]
     )
d3
Out[54]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112

To specify missing values, you can either use np.nan or NumPy's masked arrays:

In [55]:
masked_array = np.ma.asarray(values, dtype=np.object)
masked_array[(0, 2), (1, 2)] = np.ma.masked
d3 = pd.DataFrame(
        masked_array,
        columns=["birthyear", "children", "hobby", "weight"],
        index=["alice", "bob", "charles"]
     )
d3
Out[55]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3 Dancing 83
charles 1992 0 NaN 112

Instead of an ndarray, you can also pass a DataFrame object:

In [56]:
d4 = pd.DataFrame(
         d3,
         columns=["hobby", "children"],
         index=["alice", "bob"]
     )
d4
Out[56]:
hobby children
alice Biking NaN
bob Dancing 3

It is also possible to create a DataFrame with a dictionary (or list) of dictionaries (or list):

In [57]:
people = pd.DataFrame({
    "birthyear": {"alice":1985, "bob": 1984, "charles": 1992},
    "hobby": {"alice":"Biking", "bob": "Dancing"},
    "weight": {"alice":68, "bob": 83, "charles": 112},
    "children": {"bob": 3, "charles": 0}
})
people
Out[57]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112

Multi-indexing

If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:

In [58]:
d5 = pd.DataFrame(
  {
    ("public", "birthyear"):
        {("Paris","alice"):1985, ("Paris","bob"): 1984, ("London","charles"): 1992},
    ("public", "hobby"):
        {("Paris","alice"):"Biking", ("Paris","bob"): "Dancing"},
    ("private", "weight"):
        {("Paris","alice"):68, ("Paris","bob"): 83, ("London","charles"): 112},
    ("private", "children"):
        {("Paris", "alice"):np.nan, ("Paris","bob"): 3, ("London","charles"): 0}
  }
)
d5
Out[58]:
private public
children weight birthyear hobby
London charles 0.0 112 1992 NaN
Paris alice NaN 68 1985 Biking
bob 3.0 83 1984 Dancing

You can now get a DataFrame containing all the "public" columns very simply:

In [59]:
d5["public"]
Out[59]:
birthyear hobby
London charles 1992 NaN
Paris alice 1985 Biking
bob 1984 Dancing
In [60]:
d5["public", "hobby"]  # Same result as d5["public"]["hobby"]
Out[60]:
London  charles        NaN
Paris   alice       Biking
        bob        Dancing
Name: (public, hobby), dtype: object

Dropping a level

Let's look at d5 again:

In [61]:
d5
Out[61]:
private public
children weight birthyear hobby
London charles 0.0 112 1992 NaN
Paris alice NaN 68 1985 Biking
bob 3.0 83 1984 Dancing

There are two levels of columns, and two levels of indices. We can drop a column level by calling droplevel() (the same goes for indices):

In [62]:
d5.columns = d5.columns.droplevel(level = 0)
d5
Out[62]:
children weight birthyear hobby
London charles 0.0 112 1992 NaN
Paris alice NaN 68 1985 Biking
bob 3.0 83 1984 Dancing

Transposing

You can swap columns and indices using the T attribute:

In [63]:
d6 = d5.T
d6
Out[63]:
London Paris
charles alice bob
children 0 NaN 3
weight 112 68 83
birthyear 1992 1985 1984
hobby NaN Biking Dancing

Stacking and unstacking levels

Calling the stack() method will push the lowest column level after the lowest index:

In [64]:
d7 = d6.stack()
d7
Out[64]:
London Paris
children bob NaN 3
charles 0 NaN
weight alice NaN 68
bob NaN 83
charles 112 NaN
birthyear alice NaN 1985
bob NaN 1984
charles 1992 NaN
hobby alice NaN Biking
bob NaN Dancing

Note that many NaN values appeared. This makes sense because many new combinations did not exist before (eg. there was no bob in London).

Calling unstack() will do the reverse, once again creating many NaN values.

In [65]:
d8 = d7.unstack()
d8
Out[65]:
London Paris
alice bob charles alice bob charles
children None NaN 0 None 3 NaN
weight NaN NaN 112 68 83 NaN
birthyear NaN NaN 1992 1985 1984 NaN
hobby NaN NaN None Biking Dancing None

If we call unstack again, we end up with a Series object:

In [66]:
d9 = d8.unstack()
d9
Out[66]:
London  alice    children        None
                 weight           NaN
                 birthyear        NaN
                 hobby            NaN
        bob      children         NaN
                 weight           NaN
                 birthyear        NaN
                 hobby            NaN
        charles  children           0
                 weight           112
                 birthyear       1992
                 hobby           None
Paris   alice    children        None
                 weight            68
                 birthyear       1985
                 hobby         Biking
        bob      children           3
                 weight            83
                 birthyear       1984
                 hobby        Dancing
        charles  children         NaN
                 weight           NaN
                 birthyear        NaN
                 hobby           None
dtype: object

The stack() and unstack() methods let you select the level to stack/unstack. You can even stack/unstack multiple levels at once:

In [67]:
d10 = d9.unstack(level = (0,1))
d10
Out[67]:
London Paris
alice bob charles alice bob charles
children None NaN 0 None 3 NaN
weight NaN NaN 112 68 83 NaN
birthyear NaN NaN 1992 1985 1984 NaN
hobby NaN NaN None Biking Dancing None

Most methods return modified copies

As you may have noticed, the stack() and unstack() methods do not modify the object they apply to. Instead, they work on a copy and return that copy. This is true of most methods in pandas.

Accessing rows

Let's go back to the people DataFrame:

In [68]:
people
Out[68]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112

The loc attribute lets you access rows instead of columns. The result is a Series object in which the DataFrame's column names are mapped to row index labels:

In [69]:
people.loc["charles"]
Out[69]:
birthyear    1992
children        0
hobby         NaN
weight        112
Name: charles, dtype: object

You can also access rows by integer location using the iloc attribute:

In [70]:
people.iloc[2]
Out[70]:
birthyear    1992
children        0
hobby         NaN
weight        112
Name: charles, dtype: object

You can also get a slice of rows, and this returns a DataFrame object:

In [71]:
people.iloc[1:3]
Out[71]:
birthyear children hobby weight
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112

Finally, you can pass a boolean array to get the matching rows:

In [72]:
people[np.array([True, False, True])]
Out[72]:
birthyear children hobby weight
alice 1985 NaN Biking 68
charles 1992 0.0 NaN 112

This is most useful when combined with boolean expressions:

In [73]:
people[people["birthyear"] < 1990]
Out[73]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83

Adding and removing columns

You can generally treat DataFrame objects like dictionaries of Series, so the following work fine:

In [74]:
people
Out[74]:
birthyear children hobby weight
alice 1985 NaN Biking 68
bob 1984 3.0 Dancing 83
charles 1992 0.0 NaN 112
In [75]:
people["age"] = 2018 - people["birthyear"]  # adds a new column "age"
people["over 30"] = people["age"] > 30      # adds another column "over 30"
birthyears = people.pop("birthyear")
del people["children"]

people
Out[75]:
hobby weight age over 30
alice Biking 68 33 True
bob Dancing 83 34 True
charles NaN 112 26 False
In [76]:
birthyears
Out[76]:
alice      1985
bob        1984
charles    1992
Name: birthyear, dtype: int64

When you add a new colum, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:

In [77]:
people["pets"] = pd.Series({"bob": 0, "charles": 5, "eugene":1})  # alice is missing, eugene is ignored
people
Out[77]:
hobby weight age over 30 pets
alice Biking 68 33 True NaN
bob Dancing 83 34 True 0.0
charles NaN 112 26 False 5.0

When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the insert() method:

In [78]:
people.insert(1, "height", [172, 181, 185])
people
Out[78]:
hobby height weight age over 30 pets
alice Biking 172 68 33 True NaN
bob Dancing 181 83 34 True 0.0
charles NaN 185 112 26 False 5.0

Assigning new columns

You can also create new columns by calling the assign() method. Note that this returns a new DataFrame object, the original is not modified:

In [79]:
people.assign(
    body_mass_index = people["weight"] / (people["height"] / 100) ** 2,
    has_pets = people["pets"] > 0
)
Out[79]:
hobby height weight age over 30 pets body_mass_index has_pets
alice Biking 172 68 33 True NaN 22.985398 False
bob Dancing 181 83 34 True 0.0 25.335002 False
charles NaN 185 112 26 False 5.0 32.724617 True

Note that you cannot access columns created within the same assignment:

In [80]:
try:
    people.assign(
        body_mass_index = people["weight"] / (people["height"] / 100) ** 2,
        overweight = people["body_mass_index"] > 25
    )
except KeyError as e:
    print("Key error:", e)
Key error: 'body_mass_index'

The solution is to split this assignment in two consecutive assignments:

In [81]:
d6 = people.assign(body_mass_index = people["weight"] / (people["height"] / 100) ** 2)
d6.assign(overweight = d6["body_mass_index"] > 25)
Out[81]:
hobby height weight age over 30 pets body_mass_index overweight
alice Biking 172 68 33 True NaN 22.985398 False
bob Dancing 181 83 34 True 0.0 25.335002 True
charles NaN 185 112 26 False 5.0 32.724617 True

Having to create a temporary variable d6 is not very convenient. You may want to just chain the assigment calls, but it does not work because the people object is not actually modified by the first assignment:

In [82]:
try:
    (people
         .assign(body_mass_index = people["weight"] / (people["height"] / 100) ** 2)
         .assign(overweight = people["body_mass_index"] > 25)
    )
except KeyError as e:
    print("Key error:", e)
Key error: 'body_mass_index'

But fear not, there is a simple solution. You can pass a function to the assign() method (typically a lambda function), and this function will be called with the DataFrame as a parameter:

In [83]:
(people
     .assign(body_mass_index = lambda df: df["weight"] / (df["height"] / 100) ** 2)
     .assign(overweight = lambda df: df["body_mass_index"] > 25)
)
Out[83]:
hobby height weight age over 30 pets body_mass_index overweight
alice Biking 172 68 33 True NaN 22.985398 False
bob Dancing 181 83 34 True 0.0 25.335002 True
charles NaN 185 112 26 False 5.0 32.724617 True

Problem solved!

Evaluating an expression

A great feature supported by pandas is expression evaluation. This relies on the numexpr library which must be installed.

In [84]:
people.eval("weight / (height/100) ** 2 > 25")
Out[84]:
alice      False
bob         True
charles     True
dtype: bool

Assignment expressions are also supported. Let's set inplace=True to directly modify the DataFrame rather than getting a modified copy:

In [85]:
people.eval("body_mass_index = weight / (height/100) ** 2", inplace=True)
people
Out[85]:
hobby height weight age over 30 pets body_mass_index
alice Biking 172 68 33 True NaN 22.985398
bob Dancing 181 83 34 True 0.0 25.335002
charles NaN 185 112 26 False 5.0 32.724617

You can use a local or global variable in an expression by prefixing it with '@':

In [86]:
overweight_threshold = 30
people.eval("overweight = body_mass_index > @overweight_threshold", inplace=True)
people
Out[86]:
hobby height weight age over 30 pets body_mass_index overweight
alice Biking 172 68 33 True NaN 22.985398 False
bob Dancing 181 83 34 True 0.0 25.335002 False
charles NaN 185 112 26 False 5.0 32.724617 True

Querying a DataFrame

The query() method lets you filter a DataFrame based on a query expression:

In [87]:
people.query("age > 30 and pets == 0")
Out[87]:
hobby height weight age over 30 pets body_mass_index overweight
bob Dancing 181 83 34 True 0.0 25.335002 False

Sorting a DataFrame

You can sort a DataFrame by calling its sort_index method. By default it sorts the rows by their index label, in ascending order, but let's reverse the order:

In [88]:
people.sort_index(ascending=False)
Out[88]:
hobby height weight age over 30 pets body_mass_index overweight
charles NaN 185 112 26 False 5.0 32.724617 True
bob Dancing 181 83 34 True 0.0 25.335002 False
alice Biking 172 68 33 True NaN 22.985398 False

Note that sort_index returned a sorted copy of the DataFrame. To modify people directly, we can set the inplace argument to True. Also, we can sort the columns instead of the rows by setting axis=1:

In [89]:
people.sort_index(axis=1, inplace=True)
people
Out[89]:
age body_mass_index height hobby over 30 overweight pets weight
alice 33 22.985398 172 Biking True False NaN 68
bob 34 25.335002 181 Dancing True False 0.0 83
charles 26 32.724617 185 NaN False True 5.0 112

To sort the DataFrame by the values instead of the labels, we can use sort_values and specify the column to sort by:

In [90]:
people.sort_values(by="age", inplace=True)
people
Out[90]:
age body_mass_index height hobby over 30 overweight pets weight
charles 26 32.724617 185 NaN False True 5.0 112
alice 33 22.985398 172 Biking True False NaN 68
bob 34 25.335002 181 Dancing True False 0.0 83

Plotting a DataFrame

Just like for Series, pandas makes it easy to draw nice graphs based on a DataFrame.

For example, it is trivial to create a line plot from a DataFrame's data by calling its plot method:

In [91]:
people.plot(kind = "line", x = "body_mass_index", y = ["height", "weight"])
plt.show()

You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the s argument of matplotlib's scatter() function:

In [92]:
people.plot(kind = "scatter", x = "height", y = "weight", s=[40, 120, 200])
plt.show()

Again, there are way too many options to list here: the best option is to scroll through the Visualization page in pandas' documentation, find the plot you are interested in and look at the example code.

Operations on DataFrames

Although DataFrames do not try to mimick NumPy arrays, there are a few similarities. Let's create a DataFrame to demonstrate this:

In [93]:
grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])
grades = pd.DataFrame(grades_array, columns=["sep", "oct", "nov"], index=["alice","bob","charles","darwin"])
grades
Out[93]:
sep oct nov
alice 8 8 9
bob 10 9 9
charles 4 8 2
darwin 9 10 10

You can apply NumPy mathematical functions on a DataFrame: the function is applied to all values:

In [94]:
np.sqrt(grades)
Out[94]:
sep oct nov
alice 2.828427 2.828427 3.000000
bob 3.162278 3.000000 3.000000
charles 2.000000 2.828427 1.414214
darwin 3.000000 3.162278 3.162278

Similarly, adding a single value to a DataFrame will add that value to all elements in the DataFrame. This is called broadcasting:

In [95]:
grades + 1
Out[95]:
sep oct nov
alice 9 9 10
bob 11 10 10
charles 5 9 3
darwin 10 11 11

Of course, the same is true for all other binary operations, including arithmetic (*,/,**...) and conditional (>, ==...) operations:

In [96]:
grades >= 5
Out[96]:
sep oct nov
alice True True True
bob True True True
charles False True False
darwin True True True

Aggregation operations, such as computing the max, the sum or the mean of a DataFrame, apply to each column, and you get back a Series object:

In [97]:
grades.mean()
Out[97]:
sep    7.75
oct    8.75
nov    7.50
dtype: float64

The all method is also an aggregation operation: it checks whether all values are True or not. Let's see during which months all students got a grade greater than 5:

In [98]:
(grades > 5).all()
Out[98]:
sep    False
oct     True
nov    False
dtype: bool

Most of these functions take an optional axis parameter which lets you specify along which axis of the DataFrame you want the operation executed. The default is axis=0, meaning that the operation is executed vertically (on each column). You can set axis=1 to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than 5:

In [99]:
(grades > 5).all(axis = 1)
Out[99]:
alice       True
bob         True
charles    False
darwin      True
dtype: bool

The any method returns True if any value is True. Let's see who got at least one grade 10:

In [100]:
(grades == 10).any(axis = 1)
Out[100]:
alice      False
bob         True
charles    False
darwin      True
dtype: bool

If you add a Series object to a DataFrame (or execute any other binary operation), pandas attempts to broadcast the operation to all rows in the DataFrame. This only works if the Series has the same size as the DataFrames rows. For example, let's substract the mean of the DataFrame (a Series object) from the DataFrame:

In [101]:
grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]
Out[101]:
sep oct nov
alice 0.25 -0.75 1.5
bob 2.25 0.25 1.5
charles -3.75 -0.75 -5.5
darwin 1.25 1.25 2.5

We substracted 7.75 from all September grades, 8.75 from October grades and 7.50 from November grades. It is equivalent to substracting this DataFrame:

In [102]:
pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)
Out[102]:
sep oct nov
alice 7.75 8.75 7.5
bob 7.75 8.75 7.5
charles 7.75 8.75 7.5
darwin 7.75 8.75 7.5

If you want to substract the global mean from every grade, here is one way to do it:

In [103]:
grades - grades.values.mean() # substracts the global mean (8.00) from all grades
Out[103]:
sep oct nov
alice 0.0 0.0 1.0
bob 2.0 1.0 1.0
charles -4.0 0.0 -6.0
darwin 1.0 2.0 2.0

Automatic alignment

Similar to Series, when operating on multiple DataFrames, pandas automatically aligns them by row index label, but also by column names. Let's create a DataFrame with bonus points for each person from October to December:

In [104]:
bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])
bonus_points = pd.DataFrame(bonus_array, columns=["oct", "nov", "dec"], index=["bob","colin", "darwin", "charles"])
bonus_points
Out[104]:
oct nov dec
bob 0.0 NaN 2.0
colin NaN 1.0 0.0
darwin 0.0 1.0 0.0
charles 3.0 3.0 0.0
In [105]:
grades + bonus_points
Out[105]:
dec nov oct sep
alice NaN NaN NaN NaN
bob NaN NaN 9.0 NaN
charles NaN 5.0 11.0 NaN
colin NaN NaN NaN NaN
darwin NaN 11.0 10.0 NaN

Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the DataFrames, some columns and rows were only present on one side, and thus they were considered missing on the other side (NaN). Then adding NaN to a number results in NaN, hence the result.

Handling missing data

Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.

Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of NaN. We can replace all NaN values by a any value using the fillna() method:

In [106]:
(grades + bonus_points).fillna(0)
Out[106]:
dec nov oct sep
alice 0.0 0.0 0.0 0.0
bob 0.0 0.0 9.0 0.0
charles 0.0 5.0 11.0 0.0
colin 0.0 0.0 0.0 0.0
darwin 0.0 11.0 10.0 0.0

It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:

In [107]:
fixed_bonus_points = bonus_points.fillna(0)
fixed_bonus_points.insert(0, "sep", 0)
fixed_bonus_points.loc["alice"] = 0
grades + fixed_bonus_points
Out[107]:
dec nov oct sep
alice NaN 9.0 8.0 8.0
bob NaN 9.0 9.0 10.0
charles NaN 5.0 11.0 4.0
colin NaN NaN NaN NaN
darwin NaN 11.0 10.0 9.0

That's much better: although we made up some data, we have not been too unfair.

Another way to handle missing data is to interpolate. Let's look at the bonus_points DataFrame again:

In [108]:
bonus_points
Out[108]:
oct nov dec
bob 0.0 NaN 2.0
colin NaN 1.0 0.0
darwin 0.0 1.0 0.0
charles 3.0 3.0 0.0

Now let's call the interpolate method. By default, it interpolates vertically (axis=0), so let's tell it to interpolate horizontally (axis=1).

In [109]:
bonus_points.interpolate(axis=1)
Out[109]:
oct nov dec
bob 0.0 1.0 2.0
colin NaN 1.0 0.0
darwin 0.0 1.0 0.0
charles 3.0 3.0 0.0

Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation.

In [110]:
better_bonus_points = bonus_points.copy()
better_bonus_points.insert(0, "sep", 0)
better_bonus_points.loc["alice"] = 0
better_bonus_points = better_bonus_points.interpolate(axis=1)
better_bonus_points
Out[110]:
sep oct nov dec
bob 0.0 0.0 1.0 2.0
colin 0.0 0.5 1.0 0.0
darwin 0.0 0.0 1.0 0.0
charles 0.0 3.0 3.0 0.0
alice 0.0 0.0 0.0 0.0

Great, now we have reasonable bonus points everywhere. Let's find out the final grades:

In [111]:
grades + better_bonus_points
Out[111]:
dec nov oct sep
alice NaN 9.0 8.0 8.0
bob NaN 10.0 9.0 10.0
charles NaN 5.0 11.0 4.0
colin NaN NaN NaN NaN
darwin NaN 11.0 10.0 9.0

It is slightly annoying that the September column ends up on the right. This is because the DataFrames we are adding do not have the exact same columns (the grades DataFrame is missing the "dec" column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:

In [112]:
grades["dec"] = np.nan
final_grades = grades + better_bonus_points
final_grades
Out[112]:
sep oct nov dec
alice 8.0 8.0 9.0 NaN
bob 10.0 9.0 10.0 NaN
charles 4.0 11.0 5.0 NaN
colin NaN NaN NaN NaN
darwin 9.0 10.0 11.0 NaN

There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well I guess some teachers probably do). So let's call the dropna() method to get rid of rows that are full of NaNs:

In [113]:
final_grades_clean = final_grades.dropna(how="all")
final_grades_clean
Out[113]:
sep oct nov dec
alice 8.0 8.0 9.0 NaN
bob 10.0 9.0 10.0 NaN
charles 4.0 11.0 5.0 NaN
darwin 9.0 10.0 11.0 NaN

Now let's remove columns that are full of NaNs by setting the axis argument to 1:

In [114]:
final_grades_clean = final_grades_clean.dropna(axis=1, how="all")
final_grades_clean
Out[114]:
sep oct nov
alice 8.0 8.0 9.0
bob 10.0 9.0 10.0
charles 4.0 11.0 5.0
darwin 9.0 10.0 11.0

Aggregating with groupby

Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.

First, let's add some extra data about each person so we can group them, and let's go back to the final_grades DataFrame so we can see how NaN values are handled:

In [115]:
final_grades["hobby"] = ["Biking", "Dancing", np.nan, "Dancing", "Biking"]
final_grades
Out[115]:
sep oct nov dec hobby
alice 8.0 8.0 9.0 NaN Biking
bob 10.0 9.0 10.0 NaN Dancing
charles 4.0 11.0 5.0 NaN NaN
colin NaN NaN NaN NaN Dancing
darwin 9.0 10.0 11.0 NaN Biking

Now let's group data in this DataFrame by hobby:

In [116]:
grouped_grades = final_grades.groupby("hobby")
grouped_grades
Out[116]:
<pandas.core.groupby.DataFrameGroupBy object at 0x10b680e10>

We are ready to compute the average grade per hobby:

In [117]:
grouped_grades.mean()
Out[117]:
sep oct nov dec
hobby
Biking 8.5 9.0 10.0 NaN
Dancing 10.0 9.0 10.0 NaN

That was easy! Note that the NaN values have simply been skipped when computing the means.

Pivot tables

Pandas supports spreadsheet-like pivot tables that allow quick data summarization. To illustrate this, let's create a simple DataFrame:

In [118]:
bonus_points
Out[118]:
oct nov dec
bob 0.0 NaN 2.0
colin NaN 1.0 0.0
darwin 0.0 1.0 0.0
charles 3.0 3.0 0.0
In [119]:
more_grades = final_grades_clean.stack().reset_index()
more_grades.columns = ["name", "month", "grade"]
more_grades["bonus"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]
more_grades
Out[119]:
name month grade bonus
0 alice sep 8.0 NaN
1 alice oct 8.0 NaN
2 alice nov 9.0 NaN
3 bob sep 10.0 0.0
4 bob oct 9.0 NaN
5 bob nov 10.0 2.0
6 charles sep 4.0 3.0
7 charles oct 11.0 3.0
8 charles nov 5.0 0.0
9 darwin sep 9.0 0.0
10 darwin oct 10.0 1.0
11 darwin nov 11.0 0.0

Now we can call the pd.pivot_table() function for this DataFrame, asking to group by the name column. By default, pivot_table() computes the mean of each numeric column:

In [120]:
pd.pivot_table(more_grades, index="name")
Out[120]:
bonus grade
name
alice NaN 8.333333
bob 1.000000 9.666667
charles 2.000000 6.666667
darwin 0.333333 10.000000

We can change the aggregation function by setting the aggfunc argument, and we can also specify the list of columns whose values will be aggregated:

In [121]:
pd.pivot_table(more_grades, index="name", values=["grade","bonus"], aggfunc=np.max)
Out[121]:
bonus grade
name
alice NaN 9.0
bob 2.0 10.0
charles 3.0 11.0
darwin 1.0 11.0

We can also specify the columns to aggregate over horizontally, and request the grand totals for each row and column by setting margins=True:

In [122]:
pd.pivot_table(more_grades, index="name", values="grade", columns="month", margins=True)
Out[122]:
month nov oct sep All
name
alice 9.00 8.0 8.00 8.333333
bob 10.00 9.0 10.00 9.666667
charles 5.00 11.0 4.00 6.666667
darwin 11.00 10.0 9.00 10.000000
All 8.75 9.5 7.75 8.666667

Finally, we can specify multiple index or column names, and pandas will create multi-level indices:

In [123]:
pd.pivot_table(more_grades, index=("name", "month"), margins=True)
Out[123]:
bonus grade
name month
alice nov NaN 9.00
oct NaN 8.00
sep NaN 8.00
bob nov 2.000 10.00
oct NaN 9.00
sep 0.000 10.00
charles nov 0.000 5.00
oct 3.000 11.00
sep 3.000 4.00
darwin nov 0.000 11.00
oct 1.000 10.00
sep 0.000 9.00
All 1.125 8.75

Overview functions

When dealing with large DataFrames, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large DataFrame with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the DataFrame:

In [124]:
much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))
large_df = pd.DataFrame(much_data, columns=list("ABCDEFGHIJKLMNOPQRSTUVWXYZ"))
large_df[large_df % 16 == 0] = np.nan
large_df.insert(3,"some_text", "Blabla")
large_df
Out[124]:
A B C some_text D E F G H I ... Q R S T U V W X Y Z
0 NaN 11.0 44.0 Blabla 99.0 NaN 88.0 22.0 165.0 143.0 ... 11.0 NaN 11.0 44.0 99.0 NaN 88.0 22.0 165.0 143.0
1 11.0 22.0 55.0 Blabla 110.0 NaN 99.0 33.0 NaN 154.0 ... 22.0 11.0 22.0 55.0 110.0 NaN 99.0 33.0 NaN 154.0
2 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
3 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN
4 44.0 55.0 88.0 Blabla 143.0 33.0 132.0 66.0 22.0 NaN ... 55.0 44.0 55.0 88.0 143.0 33.0 132.0 66.0 22.0 NaN
5 55.0 66.0 99.0 Blabla 154.0 44.0 143.0 77.0 33.0 11.0 ... 66.0 55.0 66.0 99.0 154.0 44.0 143.0 77.0 33.0 11.0
6 66.0 77.0 110.0 Blabla 165.0 55.0 154.0 88.0 44.0 22.0 ... 77.0 66.0 77.0 110.0 165.0 55.0 154.0 88.0 44.0 22.0
7 77.0 88.0 121.0 Blabla NaN 66.0 165.0 99.0 55.0 33.0 ... 88.0 77.0 88.0 121.0 NaN 66.0 165.0 99.0 55.0 33.0
8 88.0 99.0 132.0 Blabla NaN 77.0 NaN 110.0 66.0 44.0 ... 99.0 88.0 99.0 132.0 NaN 77.0 NaN 110.0 66.0 44.0
9 99.0 110.0 143.0 Blabla 11.0 88.0 NaN 121.0 77.0 55.0 ... 110.0 99.0 110.0 143.0 11.0 88.0 NaN 121.0 77.0 55.0
10 110.0 121.0 154.0 Blabla 22.0 99.0 11.0 132.0 88.0 66.0 ... 121.0 110.0 121.0 154.0 22.0 99.0 11.0 132.0 88.0 66.0
11 121.0 132.0 165.0 Blabla 33.0 110.0 22.0 143.0 99.0 77.0 ... 132.0 121.0 132.0 165.0 33.0 110.0 22.0 143.0 99.0 77.0
12 132.0 143.0 NaN Blabla 44.0 121.0 33.0 154.0 110.0 88.0 ... 143.0 132.0 143.0 NaN 44.0 121.0 33.0 154.0 110.0 88.0
13 143.0 154.0 NaN Blabla 55.0 132.0 44.0 165.0 121.0 99.0 ... 154.0 143.0 154.0 NaN 55.0 132.0 44.0 165.0 121.0 99.0
14 154.0 165.0 11.0 Blabla 66.0 143.0 55.0 NaN 132.0 110.0 ... 165.0 154.0 165.0 11.0 66.0 143.0 55.0 NaN 132.0 110.0
15 165.0 NaN 22.0 Blabla 77.0 154.0 66.0 NaN 143.0 121.0 ... NaN 165.0 NaN 22.0 77.0 154.0 66.0 NaN 143.0 121.0
16 NaN NaN 33.0 Blabla 88.0 165.0 77.0 11.0 154.0 132.0 ... NaN NaN NaN 33.0 88.0 165.0 77.0 11.0 154.0 132.0
17 NaN 11.0 44.0 Blabla 99.0 NaN 88.0 22.0 165.0 143.0 ... 11.0 NaN 11.0 44.0 99.0 NaN 88.0 22.0 165.0 143.0
18 11.0 22.0 55.0 Blabla 110.0 NaN 99.0 33.0 NaN 154.0 ... 22.0 11.0 22.0 55.0 110.0 NaN 99.0 33.0 NaN 154.0
19 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
20 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN
21 44.0 55.0 88.0 Blabla 143.0 33.0 132.0 66.0 22.0 NaN ... 55.0 44.0 55.0 88.0 143.0 33.0 132.0 66.0 22.0 NaN
22 55.0 66.0 99.0 Blabla 154.0 44.0 143.0 77.0 33.0 11.0 ... 66.0 55.0 66.0 99.0 154.0 44.0 143.0 77.0 33.0 11.0
23 66.0 77.0 110.0 Blabla 165.0 55.0 154.0 88.0 44.0 22.0 ... 77.0 66.0 77.0 110.0 165.0 55.0 154.0 88.0 44.0 22.0
24 77.0 88.0 121.0 Blabla NaN 66.0 165.0 99.0 55.0 33.0 ... 88.0 77.0 88.0 121.0 NaN 66.0 165.0 99.0 55.0 33.0
25 88.0 99.0 132.0 Blabla NaN 77.0 NaN 110.0 66.0 44.0 ... 99.0 88.0 99.0 132.0 NaN 77.0 NaN 110.0 66.0 44.0
26 99.0 110.0 143.0 Blabla 11.0 88.0 NaN 121.0 77.0 55.0 ... 110.0 99.0 110.0 143.0 11.0 88.0 NaN 121.0 77.0 55.0
27 110.0 121.0 154.0 Blabla 22.0 99.0 11.0 132.0 88.0 66.0 ... 121.0 110.0 121.0 154.0 22.0 99.0 11.0 132.0 88.0 66.0
28 121.0 132.0 165.0 Blabla 33.0 110.0 22.0 143.0 99.0 77.0 ... 132.0 121.0 132.0 165.0 33.0 110.0 22.0 143.0 99.0 77.0
29 132.0 143.0 NaN Blabla 44.0 121.0 33.0 154.0 110.0 88.0 ... 143.0 132.0 143.0 NaN 44.0 121.0 33.0 154.0 110.0 88.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9970 88.0 99.0 132.0 Blabla NaN 77.0 NaN 110.0 66.0 44.0 ... 99.0 88.0 99.0 132.0 NaN 77.0 NaN 110.0 66.0 44.0
9971 99.0 110.0 143.0 Blabla 11.0 88.0 NaN 121.0 77.0 55.0 ... 110.0 99.0 110.0 143.0 11.0 88.0 NaN 121.0 77.0 55.0
9972 110.0 121.0 154.0 Blabla 22.0 99.0 11.0 132.0 88.0 66.0 ... 121.0 110.0 121.0 154.0 22.0 99.0 11.0 132.0 88.0 66.0
9973 121.0 132.0 165.0 Blabla 33.0 110.0 22.0 143.0 99.0 77.0 ... 132.0 121.0 132.0 165.0 33.0 110.0 22.0 143.0 99.0 77.0
9974 132.0 143.0 NaN Blabla 44.0 121.0 33.0 154.0 110.0 88.0 ... 143.0 132.0 143.0 NaN 44.0 121.0 33.0 154.0 110.0 88.0
9975 143.0 154.0 NaN Blabla 55.0 132.0 44.0 165.0 121.0 99.0 ... 154.0 143.0 154.0 NaN 55.0 132.0 44.0 165.0 121.0 99.0
9976 154.0 165.0 11.0 Blabla 66.0 143.0 55.0 NaN 132.0 110.0 ... 165.0 154.0 165.0 11.0 66.0 143.0 55.0 NaN 132.0 110.0
9977 165.0 NaN 22.0 Blabla 77.0 154.0 66.0 NaN 143.0 121.0 ... NaN 165.0 NaN 22.0 77.0 154.0 66.0 NaN 143.0 121.0
9978 NaN NaN 33.0 Blabla 88.0 165.0 77.0 11.0 154.0 132.0 ... NaN NaN NaN 33.0 88.0 165.0 77.0 11.0 154.0 132.0
9979 NaN 11.0 44.0 Blabla 99.0 NaN 88.0 22.0 165.0 143.0 ... 11.0 NaN 11.0 44.0 99.0 NaN 88.0 22.0 165.0 143.0
9980 11.0 22.0 55.0 Blabla 110.0 NaN 99.0 33.0 NaN 154.0 ... 22.0 11.0 22.0 55.0 110.0 NaN 99.0 33.0 NaN 154.0
9981 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
9982 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN
9983 44.0 55.0 88.0 Blabla 143.0 33.0 132.0 66.0 22.0 NaN ... 55.0 44.0 55.0 88.0 143.0 33.0 132.0 66.0 22.0 NaN
9984 55.0 66.0 99.0 Blabla 154.0 44.0 143.0 77.0 33.0 11.0 ... 66.0 55.0 66.0 99.0 154.0 44.0 143.0 77.0 33.0 11.0
9985 66.0 77.0 110.0 Blabla 165.0 55.0 154.0 88.0 44.0 22.0 ... 77.0 66.0 77.0 110.0 165.0 55.0 154.0 88.0 44.0 22.0
9986 77.0 88.0 121.0 Blabla NaN 66.0 165.0 99.0 55.0 33.0 ... 88.0 77.0 88.0 121.0 NaN 66.0 165.0 99.0 55.0 33.0
9987 88.0 99.0 132.0 Blabla NaN 77.0 NaN 110.0 66.0 44.0 ... 99.0 88.0 99.0 132.0 NaN 77.0 NaN 110.0 66.0 44.0
9988 99.0 110.0 143.0 Blabla 11.0 88.0 NaN 121.0 77.0 55.0 ... 110.0 99.0 110.0 143.0 11.0 88.0 NaN 121.0 77.0 55.0
9989 110.0 121.0 154.0 Blabla 22.0 99.0 11.0 132.0 88.0 66.0 ... 121.0 110.0 121.0 154.0 22.0 99.0 11.0 132.0 88.0 66.0
9990 121.0 132.0 165.0 Blabla 33.0 110.0 22.0 143.0 99.0 77.0 ... 132.0 121.0 132.0 165.0 33.0 110.0 22.0 143.0 99.0 77.0
9991 132.0 143.0 NaN Blabla 44.0 121.0 33.0 154.0 110.0 88.0 ... 143.0 132.0 143.0 NaN 44.0 121.0 33.0 154.0 110.0 88.0
9992 143.0 154.0 NaN Blabla 55.0 132.0 44.0 165.0 121.0 99.0 ... 154.0 143.0 154.0 NaN 55.0 132.0 44.0 165.0 121.0 99.0
9993 154.0 165.0 11.0 Blabla 66.0 143.0 55.0 NaN 132.0 110.0 ... 165.0 154.0 165.0 11.0 66.0 143.0 55.0 NaN 132.0 110.0
9994 165.0 NaN 22.0 Blabla 77.0 154.0 66.0 NaN 143.0 121.0 ... NaN 165.0 NaN 22.0 77.0 154.0 66.0 NaN 143.0 121.0
9995 NaN NaN 33.0 Blabla 88.0 165.0 77.0 11.0 154.0 132.0 ... NaN NaN NaN 33.0 88.0 165.0 77.0 11.0 154.0 132.0
9996 NaN 11.0 44.0 Blabla 99.0 NaN 88.0 22.0 165.0 143.0 ... 11.0 NaN 11.0 44.0 99.0 NaN 88.0 22.0 165.0 143.0
9997 11.0 22.0 55.0 Blabla 110.0 NaN 99.0 33.0 NaN 154.0 ... 22.0 11.0 22.0 55.0 110.0 NaN 99.0 33.0 NaN 154.0
9998 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
9999 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN

10000 rows × 27 columns

The head() method returns the top 5 rows:

In [125]:
large_df.head()
Out[125]:
A B C some_text D E F G H I ... Q R S T U V W X Y Z
0 NaN 11.0 44.0 Blabla 99.0 NaN 88.0 22.0 165.0 143.0 ... 11.0 NaN 11.0 44.0 99.0 NaN 88.0 22.0 165.0 143.0
1 11.0 22.0 55.0 Blabla 110.0 NaN 99.0 33.0 NaN 154.0 ... 22.0 11.0 22.0 55.0 110.0 NaN 99.0 33.0 NaN 154.0
2 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
3 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN
4 44.0 55.0 88.0 Blabla 143.0 33.0 132.0 66.0 22.0 NaN ... 55.0 44.0 55.0 88.0 143.0 33.0 132.0 66.0 22.0 NaN

5 rows × 27 columns

Of course there's also a tail() function to view the bottom 5 rows. You can pass the number of rows you want:

In [126]:
large_df.tail(n=2)
Out[126]:
A B C some_text D E F G H I ... Q R S T U V W X Y Z
9998 22.0 33.0 66.0 Blabla 121.0 11.0 110.0 44.0 NaN 165.0 ... 33.0 22.0 33.0 66.0 121.0 11.0 110.0 44.0 NaN 165.0
9999 33.0 44.0 77.0 Blabla 132.0 22.0 121.0 55.0 11.0 NaN ... 44.0 33.0 44.0 77.0 132.0 22.0 121.0 55.0 11.0 NaN

2 rows × 27 columns

The info() method prints out a summary of each columns contents:

In [127]:
large_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 27 columns):
A            8823 non-null float64
B            8824 non-null float64
C            8824 non-null float64
some_text    10000 non-null object
D            8824 non-null float64
E            8822 non-null float64
F            8824 non-null float64
G            8824 non-null float64
H            8822 non-null float64
I            8823 non-null float64
J            8823 non-null float64
K            8822 non-null float64
L            8824 non-null float64
M            8824 non-null float64
N            8822 non-null float64
O            8824 non-null float64
P            8824 non-null float64
Q            8824 non-null float64
R            8823 non-null float64
S            8824 non-null float64
T            8824 non-null float64
U            8824 non-null float64
V            8822 non-null float64
W            8824 non-null float64
X            8824 non-null float64
Y            8822 non-null float64
Z            8823 non-null float64
dtypes: float64(26), object(1)
memory usage: 2.1+ MB

Finally, the describe() method gives a nice overview of the main aggregated values over each column:

  • count: number of non-null (not NaN) values
  • mean: mean of non-null values
  • std: standard deviation of non-null values
  • min: minimum of non-null values
  • 25%, 50%, 75%: 25th, 50th and 75th percentile of non-null values
  • max: maximum of non-null values
In [128]:
large_df.describe()
Out[128]:
A B C D E F G H I J ... Q R S T U V W X Y Z
count 8823.000000 8824.000000 8824.000000 8824.000000 8822.000000 8824.000000 8824.000000 8822.000000 8823.000000 8823.000000 ... 8824.000000 8823.000000 8824.000000 8824.000000 8824.000000 8822.000000 8824.000000 8824.000000 8822.000000 8823.000000
mean 87.977559 87.972575 87.987534 88.012466 87.983791 88.007480 87.977561 88.000000 88.022441 88.022441 ... 87.972575 87.977559 87.972575 87.987534 88.012466 87.983791 88.007480 87.977561 88.000000 88.022441
std 47.535911 47.535523 47.521679 47.521679 47.535001 47.519371 47.529755 47.536879 47.535911 47.535911 ... 47.535523 47.535911 47.535523 47.521679 47.521679 47.535001 47.519371 47.529755 47.536879 47.535911
min 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 ... 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000
25% 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 ... 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000 44.000000
50% 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 ... 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000 88.000000
75% 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 ... 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000 132.000000
max 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 ... 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000 165.000000

8 rows × 26 columns

Saving & loading

Pandas can save DataFrames to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a DataFrame to demonstrate this:

In [129]:
my_df = pd.DataFrame(
    [["Biking", 68.5, 1985, np.nan], ["Dancing", 83.1, 1984, 3]], 
    columns=["hobby","weight","birthyear","children"],
    index=["alice", "bob"]
)
my_df
Out[129]:
hobby weight birthyear children
alice Biking 68.5 1985 NaN
bob Dancing 83.1 1984 3.0

Saving

Let's save it to CSV, HTML and JSON:

In [130]:
my_df.to_csv("my_df.csv")
my_df.to_html("my_df.html")
my_df.to_json("my_df.json")

Done! Let's take a peek at what was saved:

In [131]:
for filename in ("my_df.csv", "my_df.html", "my_df.json"):
    print("#", filename)
    with open(filename, "rt") as f:
        print(f.read())
        print()
# my_df.csv
,hobby,weight,birthyear,children
alice,Biking,68.5,1985,
bob,Dancing,83.1,1984,3.0


# my_df.html
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>hobby</th>
      <th>weight</th>
      <th>birthyear</th>
      <th>children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>alice</th>
      <td>Biking</td>
      <td>68.5</td>
      <td>1985</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>bob</th>
      <td>Dancing</td>
      <td>83.1</td>
      <td>1984</td>
      <td>3.0</td>
    </tr>
  </tbody>
</table>

# my_df.json
{"hobby":{"alice":"Biking","bob":"Dancing"},"weight":{"alice":68.5,"bob":83.1},"birthyear":{"alice":1985,"bob":1984},"children":{"alice":null,"bob":3.0}}

Note that the index is saved as the first column (with no name) in a CSV file, as <th> tags in HTML and as keys in JSON.

Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:

In [132]:
try:
    my_df.to_excel("my_df.xlsx", sheet_name='People')
except ImportError as e:
    print(e)
No module named 'openpyxl'

Loading

Now let's load our CSV file back into a DataFrame:

In [133]:
my_df_loaded = pd.read_csv("my_df.csv", index_col=0)
my_df_loaded
Out[133]:
hobby weight birthyear children
alice Biking 68.5 1985 NaN
bob Dancing 83.1 1984 3.0

As you might guess, there are similar read_json, read_html, read_excel functions as well. We can also read data straight from the Internet. For example, let's load the top 1,000 U.S. cities from github:

In [134]:
us_cities = None
try:
    csv_url = "https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv"
    us_cities = pd.read_csv(csv_url, index_col=0)
    us_cities = us_cities.head()
except IOError as e:
    print(e)
us_cities
Out[134]:
State Population lat lon
City
Marysville Washington 63269 48.051764 -122.177082
Perris California 72326 33.782519 -117.228648
Cleveland Ohio 390113 41.499320 -81.694361
Worcester Massachusetts 182544 42.262593 -71.802293
Columbia South Carolina 133358 34.000710 -81.034814

There are more options available, in particular regarding datetime format. Check out the documentation for more details.

Combining DataFrames

SQL-like joins

One powerful feature of pandas is it's ability to perform SQL-like joins on DataFrames. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple simple DataFrames:

In [135]:
city_loc = pd.DataFrame(
    [
        ["CA", "San Francisco", 37.781334, -122.416728],
        ["NY", "New York", 40.705649, -74.008344],
        ["FL", "Miami", 25.791100, -80.320733],
        ["OH", "Cleveland", 41.473508, -81.739791],
        ["UT", "Salt Lake City", 40.755851, -111.896657]
    ], columns=["state", "city", "lat", "lng"])
city_loc
Out[135]:
state city lat lng
0 CA San Francisco 37.781334 -122.416728
1 NY New York 40.705649 -74.008344
2 FL Miami 25.791100 -80.320733
3 OH Cleveland 41.473508 -81.739791
4 UT Salt Lake City 40.755851 -111.896657
In [136]:
city_pop = pd.DataFrame(
    [
        [808976, "San Francisco", "California"],
        [8363710, "New York", "New-York"],
        [413201, "Miami", "Florida"],
        [2242193, "Houston", "Texas"]
    ], index=[3,4,5,6], columns=["population", "city", "state"])
city_pop
Out[136]:
population city state
3 808976 San Francisco California
4 8363710 New York New-York
5 413201 Miami Florida
6 2242193 Houston Texas

Now let's join these DataFrames using the merge() function:

In [137]:
pd.merge(left=city_loc, right=city_pop, on="city")
Out[137]:
state_x city lat lng population state_y
0 CA San Francisco 37.781334 -122.416728 808976 California
1 NY New York 40.705649 -74.008344 8363710 New-York
2 FL Miami 25.791100 -80.320733 413201 Florida

Note that both DataFrames have a column named state, so in the result they got renamed to state_x and state_y.

Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in both DataFrames. This is the equivalent of a SQL INNER JOIN. If you want a FULL OUTER JOIN, where no city gets dropped and NaN values are added, you must specify how="outer":

In [138]:
all_cities = pd.merge(left=city_loc, right=city_pop, on="city", how="outer")
all_cities
Out[138]:
state_x city lat lng population state_y
0 CA San Francisco 37.781334 -122.416728 808976.0 California
1 NY New York 40.705649 -74.008344 8363710.0 New-York
2 FL Miami 25.791100 -80.320733 413201.0 Florida
3 OH Cleveland 41.473508 -81.739791 NaN NaN
4 UT Salt Lake City 40.755851 -111.896657 NaN NaN
5 NaN Houston NaN NaN 2242193.0 Texas

Of course LEFT OUTER JOIN is also available by setting how="left": only the cities present in the left DataFrame end up in the result. Similarly, with how="right" only cities in the right DataFrame appear in the result. For example:

In [139]:
pd.merge(left=city_loc, right=city_pop, on="city", how="right")
Out[139]:
state_x city lat lng population state_y
0 CA San Francisco 37.781334 -122.416728 808976 California
1 NY New York 40.705649 -74.008344 8363710 New-York
2 FL Miami 25.791100 -80.320733 413201 Florida
3 NaN Houston NaN NaN 2242193 Texas

If the key to join on is actually in one (or both) DataFrame's index, you must use left_index=True and/or right_index=True. If the key column names differ, you must use left_on and right_on. For example:

In [140]:
city_pop2 = city_pop.copy()
city_pop2.columns = ["population", "name", "state"]
pd.merge(left=city_loc, right=city_pop2, left_on="city", right_on="name")
Out[140]:
state_x city lat lng population name state_y
0 CA San Francisco 37.781334 -122.416728 808976 San Francisco California
1 NY New York 40.705649 -74.008344 8363710 New York New-York
2 FL Miami 25.791100 -80.320733 413201 Miami Florida

Concatenation

Rather than joining DataFrames, we may just want to concatenate them. That's what concat() is for:

In [141]:
result_concat = pd.concat([city_loc, city_pop])
result_concat
Out[141]:
city lat lng population state
0 San Francisco 37.781334 -122.416728 NaN CA
1 New York 40.705649 -74.008344 NaN NY
2 Miami 25.791100 -80.320733 NaN FL
3 Cleveland 41.473508 -81.739791 NaN OH
4 Salt Lake City 40.755851 -111.896657 NaN UT
3 San Francisco NaN NaN 808976.0 California
4 New York NaN NaN 8363710.0 New-York
5 Miami NaN NaN 413201.0 Florida
6 Houston NaN NaN 2242193.0 Texas

Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (eg. 3). Pandas handles this rather gracefully:

In [142]:
result_concat.loc[3]
Out[142]:
city lat lng population state
3 Cleveland 41.473508 -81.739791 NaN OH
3 San Francisco NaN NaN 808976.0 California

Or you can tell pandas to just ignore the index:

In [143]:
pd.concat([city_loc, city_pop], ignore_index=True)
Out[143]:
city lat lng population state
0 San Francisco 37.781334 -122.416728 NaN CA
1 New York 40.705649 -74.008344 NaN NY
2 Miami 25.791100 -80.320733 NaN FL
3 Cleveland 41.473508 -81.739791 NaN OH
4 Salt Lake City 40.755851 -111.896657 NaN UT
5 San Francisco NaN NaN 808976.0 California
6 New York NaN NaN 8363710.0 New-York
7 Miami NaN NaN 413201.0 Florida
8 Houston NaN NaN 2242193.0 Texas

Notice that when a column does not exist in a DataFrame, it acts as if it was filled with NaN values. If we set join="inner", then only columns that exist in both DataFrames are returned:

In [144]:
pd.concat([city_loc, city_pop], join="inner")
Out[144]:
state city
0 CA San Francisco
1 NY New York
2 FL Miami
3 OH Cleveland
4 UT Salt Lake City
3 California San Francisco
4 New-York New York
5 Florida Miami
6 Texas Houston

You can concatenate DataFrames horizontally instead of vertically by setting axis=1:

In [145]:
pd.concat([city_loc, city_pop], axis=1)
Out[145]:
state city lat lng population city state
0 CA San Francisco 37.781334 -122.416728 NaN NaN NaN
1 NY New York 40.705649 -74.008344 NaN NaN NaN
2 FL Miami 25.791100 -80.320733 NaN NaN NaN
3 OH Cleveland 41.473508 -81.739791 808976.0 San Francisco California
4 UT Salt Lake City 40.755851 -111.896657 8363710.0 New York New-York
5 NaN NaN NaN NaN 413201.0 Miami Florida
6 NaN NaN NaN NaN 2242193.0 Houston Texas

In this case it really does not make much sense because the indices do not align well (eg. Cleveland and San Francisco end up on the same row, because they shared the index label 3). So let's reindex the DataFrames by city name before concatenating:

In [146]:
pd.concat([city_loc.set_index("city"), city_pop.set_index("city")], axis=1)
Out[146]:
state lat lng population state
Cleveland OH 41.473508 -81.739791 NaN NaN
Houston NaN NaN NaN 2242193.0 Texas
Miami FL 25.791100 -80.320733 413201.0 Florida
New York NY 40.705649 -74.008344 8363710.0 New-York
Salt Lake City UT 40.755851 -111.896657 NaN NaN
San Francisco CA 37.781334 -122.416728 808976.0 California

This looks a lot like a FULL OUTER JOIN, except that the state columns were not renamed to state_x and state_y, and the city column is now the index.

The append() method is a useful shorthand for concatenating DataFrames vertically:

In [147]:
city_loc.append(city_pop)
Out[147]:
city lat lng population state
0 San Francisco 37.781334 -122.416728 NaN CA
1 New York 40.705649 -74.008344 NaN NY
2 Miami 25.791100 -80.320733 NaN FL
3 Cleveland 41.473508 -81.739791 NaN OH
4 Salt Lake City 40.755851 -111.896657 NaN UT
3 San Francisco NaN NaN 808976.0 California
4 New York NaN NaN 8363710.0 New-York
5 Miami NaN NaN 413201.0 Florida
6 Houston NaN NaN 2242193.0 Texas

As always in pandas, the append() method does not actually modify city_loc: it works on a copy and returns the modified copy.

Categories

It is quite frequent to have values that represent categories, for example 1 for female and 2 for male, or "A" for Good, "B" for Average, "C" for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the city_pop DataFrame we created earlier, and add a column that represents a category:

In [148]:
city_eco = city_pop.copy()
city_eco["eco_code"] = [17, 17, 34, 20]
city_eco
Out[148]:
population city state eco_code
3 808976 San Francisco California 17
4 8363710 New York New-York 17
5 413201 Miami Florida 34
6 2242193 Houston Texas 20

Right now the eco_code column is full of apparently meaningless codes. Let's fix that. First, we will create a new categorical column based on the eco_codes:

In [149]:
city_eco["economy"] = city_eco["eco_code"].astype('category')
city_eco["economy"].cat.categories
Out[149]:
Int64Index([17, 20, 34], dtype='int64')

Now we can give each category a meaningful name:

In [150]:
city_eco["economy"].cat.categories = ["Finance", "Energy", "Tourism"]
city_eco
Out[150]:
population city state eco_code economy
3 808976 San Francisco California 17 Finance
4 8363710 New York New-York 17 Finance
5 413201 Miami Florida 34 Tourism
6 2242193 Houston Texas 20 Energy

Note that categorical values are sorted according to their categorical order, not their alphabetical order:

In [151]:
city_eco.sort_values(by="economy", ascending=False)
Out[151]:
population city state eco_code economy
5 413201 Miami Florida 34 Tourism
6 2242193 Houston Texas 20 Energy
4 8363710 New York New-York 17 Finance
3 808976 San Francisco California 17 Finance

What next?

As you probably noticed by now, pandas is quite a large library with many features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent documentation, in particular the Cookbook.

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