# Pandas¶

- https://pandas.pydata.org
- very high-level data containers with corresponding functionality
- many useful tools to work with time-series (look at `Series.rolling`)
- many SQL-like data operations (group, join, merge)
- Interface to a large variety of file formats (see `pd.read_[...]` functions)
- additional package with data-interface/API to many data repositories (https://pandas-datareader.readthedocs.io/en/latest/remote_data.html)

In [1]:

    import pandas as pd

## Basic Data Structures¶

### Series¶

One-dimensional ndarray with axis labels (called index).

Series can be created like an array

In [2]:

    pd.Series([11,13,17,19,23])

Out[2]:

    0    11
    1    13
    2    17
    3    19
    4    23
    dtype: int64

or, if you want a special index

In [3]:

    series = pd.Series([11,13,17,19,23], index=['a', 'b', 'c', 'd', 'e'])
    print(series)

    a    11
    b    13
    c    17
    d    19
    e    23
    dtype: int64

to get the content back you can use

In [4]:

    series.index

Out[4]:

    Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

In [5]:

    series.values

Out[5]:

    array([11, 13, 17, 19, 23])

but the power of pandas lies in all the other attributes

In [6]:

    #series. [TAB]

### DataFrame¶

The primary pandas data structure.

Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes. (index: row labels, columns: column labels) Can be thought of as a dict-like container for Series objects.

The easiest way to create a DataFrame is to read it from an input file (see later)

In addition there are many ways to create DataFrames manually. Most straight forward probably is to use a dict of iterables. (Series, Lists, Arrays). Pandas tries to choose sensible indexes.

In [7]:

    frame = pd.DataFrame({"primes": series, "fibo": [1,1,2,3,5], "0-4": range(5)})

In [8]:

    print(frame)

       primes  fibo  0-4
    a      11     1    0
    b      13     1    1
    c      17     2    2
    d      19     3    3
    e      23     5    4

# Refugee Example¶

We now want to use pandas to work with data from the World Bank. My goal is to create a plot showing the burden refugees put on different countries. For this we will plot the fraction of refugee in a give countries population versus that countries GDP.

I downloaded and extracted the following data-sets from the Worldbank website manually:

- Refugee population by country or territory of asylum: https://data.worldbank.org/indicator/SM.POP.REFG
- Population, total: https://data.worldbank.org/indicator/SP.POP.TOTL
- GDP per capita (current US$): https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

In [9]:

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt

## Loading and Accessing Data¶

loading a data file with pandas is trivial

In [10]:

    refugees = pd.read_csv("data/refugee-population.csv", skiprows=4)

In [11]:

    refugees.head()

Out[11]:

| Country Name | Country Code | Indicator Name | Indicator Code | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | ... | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Unnamed: 62
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | Aruba | ABW | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 1.0 | NaN | 2.0 | NaN | NaN | NaN
1 | Afghanistan | AFG | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | ... | 37.0 | 6434.0 | 3009.0 | 16187.0 | 16863.0 | 300423.0 | 257554.0 | 59770.0 | NaN | NaN
2 | Angola | AGO | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | ... | 14734.0 | 15155.0 | 16223.0 | 23413.0 | 23783.0 | 15474.0 | 15555.0 | 15537.0 | NaN | NaN
3 | Albania | ALB | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | ... | 70.0 | 76.0 | 82.0 | 86.0 | 93.0 | 104.0 | 104.0 | 111.0 | NaN | NaN
4 | Andorra | AND | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN

5 rows × 63 columns

As you can see pandas choose the right column labels and numbered the rows continously.

We can easily change the row labels (the index) to one of the columns.

In [12]:

    refugees.set_index(["Country Code"], inplace=True)

In [13]:

    refugees.head()

Out[13]:

| Country Name | Indicator Name | Indicator Code | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | 1966 | ... | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Unnamed: 62
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Country Code |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
ABW | Aruba | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 1.0 | NaN | 2.0 | NaN | NaN | NaN
AFG | Afghanistan | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 37.0 | 6434.0 | 3009.0 | 16187.0 | 16863.0 | 300423.0 | 257554.0 | 59770.0 | NaN | NaN
AGO | Angola | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 14734.0 | 15155.0 | 16223.0 | 23413.0 | 23783.0 | 15474.0 | 15555.0 | 15537.0 | NaN | NaN
ALB | Albania | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 70.0 | 76.0 | 82.0 | 86.0 | 93.0 | 104.0 | 104.0 | 111.0 | NaN | NaN
AND | Andorra | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN

5 rows × 62 columns

Now it's easy to select rows or columns

In [14]:

    refugees.loc[["CHE","DEU"]]

Out[14]:

| Country Name | Indicator Name | Indicator Code | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | 1966 | ... | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Unnamed: 62
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Country Code |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
CHE | Switzerland | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 46203.0 | 48813.0 | 50416.0 | 50747.0 | 52464.0 | 62620.0 | 73336.0 | 82608.0 | NaN | NaN
DEU | Germany | Refugee population by country or territory of ... | SM.POP.REFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 593799.0 | 594269.0 | 571684.0 | 589737.0 | 187567.0 | 216973.0 | 316115.0 | 669408.0 | NaN | NaN

2 rows × 62 columns

In [15]:

    refugees[["1990","2000"]].head()

Out[15]:

| 1990 | 2000
--- | --- | ---
Country Code |  |
ABW | NaN | NaN
AFG | 50.0 | NaN
AGO | 11557.0 | 12086.0
ALB | NaN | 523.0
AND | NaN | NaN

In [16]:

    refugees.get(["1990","2000"]).head()

Out[16]:

| 1990 | 2000
--- | --- | ---
Country Code |  |
ABW | NaN | NaN
AFG | 50.0 | NaN
AGO | 11557.0 | 12086.0
ALB | NaN | 523.0
AND | NaN | NaN

## Working with a Single Country¶

With this we now choose the data for one country, remove all missing values and then create a plot:

In [17]:

    che = refugees.loc["CHE"][[str(year) for year in range(1990,2018)]]

In [18]:

    che.dropna().plot()
    plt.show()

Usually it is easier to work with real datetime objects instead of strings. So we convert the index to datetime

In [19]:

    che.index.values

Out[19]:

    array(['1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',
           '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',
           '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
           '2014', '2015', '2016', '2017'], dtype=object)

In [20]:

    che.index = pd.to_datetime(che.index, format="%Y")
    print(che.index)

    DatetimeIndex(['1990-01-01', '1991-01-01', '1992-01-01', '1993-01-01',
                   '1994-01-01', '1995-01-01', '1996-01-01', '1997-01-01',
                   '1998-01-01', '1999-01-01', '2000-01-01', '2001-01-01',
                   '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01',
                   '2006-01-01', '2007-01-01', '2008-01-01', '2009-01-01',
                   '2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01',
                   '2014-01-01', '2015-01-01', '2016-01-01', '2017-01-01'],
                  dtype='datetime64[ns]', freq=None)

As mentioned in the introduction, `pandas` offers a very usefull `rolling` method

In [21]:

    che.plot()
    che.rolling(center=False,window=5).mean().plot()
    plt.show()

## Removing Unwanted Data¶

We now want to create a scatter plot with refugees divided by gdp vs. gdp-per-captita. For each data set we will use the mean of the last 7 years.

Some of the rows and columns in the World-Bank Files are of no interest for this. We can remove these easily.

### Excluding Non-Countries¶

The World-Bank provides meta-data for each country, where we can identify rows with non-countries (e.g. regional aggregates)

In [22]:

    !head data/metadata-countries_population.csv

    "ARG","Latin America & Caribbean","Upper middle income","National Institute of Statistics and Census revised national accounts from 2004-2015. Argentina, which was temporarily unclassified in July 2016 pending release of revised national accounts statistics, is classified as upper middle income for FY17 as of September 29, 2016.

We load this file and extract the two relevant columns

In [23]:

    meta = pd.read_csv("data/metadata-countries_population.csv")

In [24]:

    meta.columns

Out[24]:

    Index(['Country Code', 'Region', 'IncomeGroup', 'SpecialNotes', 'TableName',
           'Unnamed: 5'],
          dtype='object')

In [25]:

    meta = meta[['Country Code', 'Region']]

In [26]:

    meta.head()

Out[26]:

| Country Code | Region
--- | --- | ---
0 | ABW | Latin America & Caribbean
1 | AFG | South Asia
2 | AGO | Sub-Saharan Africa
3 | ALB | Europe & Central Asia
4 | AND | Europe & Central Asia

In [27]:

    meta.set_index("Country Code", inplace=True)

From this we create a list of non-countries

In [28]:

    non_countries = meta.loc[meta.Region.isnull()].index
    print(non_countries)

    Index(['ARB', 'CEB', 'CSS', 'EAP', 'EAR', 'EAS', 'ECA', 'ECS', 'EMU', 'EUU',
           'FCS', 'HIC', 'HPC', 'IBD', 'IBT', 'IDA', 'IDB', 'IDX', 'LAC', 'LCN',
           'LDC', 'LIC', 'LMC', 'LMY', 'LTE', 'MEA', 'MIC', 'MNA', 'NAC', 'OED',
           'OSS', 'PRE', 'PSS', 'PST', 'SAS', 'SSA', 'SSF', 'SST', 'TEA', 'TEC',
           'TLA', 'TMN', 'TSA', 'TSS', 'UMC', 'WLD'],
          dtype='object', name='Country Code')

and finally exclude the relevant rows

In [29]:

    refugees = refugees.drop(non_countries)

### Excluding Columns¶

The data contains a few rows with unneeded text

In [30]:

    refugees.columns

Out[30]:

    Index(['Country Name', 'Indicator Name', 'Indicator Code', '1960', '1961',
           '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970',
           '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979',
           '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988',
           '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',
           '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006',
           '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',
           '2016', '2017', 'Unnamed: 62'],
          dtype='object')

In addition, the 2017 column is empty

In [31]:

    np.any(refugees["2017"].notnull())

Out[31]:

    False

so we can create a list of all interesting columns

In [32]:

    useful_cols = []
    for year in range(2010,2017):
        useful_cols.append(str(year))

In [33]:

    useful_cols

Out[33]:

    ['2010', '2011', '2012', '2013', '2014', '2015', '2016']

with this, we:

- select the reduced datase
- switch the index to Country Code
- calculate the mean for each country

In [34]:

    refugees = refugees[useful_cols]

In [35]:

    refugee_means = refugees.mean(axis=1)

## Loading Additional Files¶

Of course we could execute these commands again manually for the two remaining data-files. However, the proper way to solve this is to create a function for this. Especially since all files have the exact same structure.

In [36]:

    def load_file(file):
        """Load and process a Worldbank File"""
        data = pd.read_csv(file, skiprows=4)
        data.set_index("Country Code", inplace=True)
        data.drop(non_countries, inplace=True)
        data = data[[str(year) for year in range(2010,2017)]]
        return data.mean(axis=1), data

In [37]:

    gdp_means, gdp = load_file("data/gdp-per-capita.csv")

In [38]:

    gdp_means.head()

Out[38]:

    Country Code
    ABW    24798.330391
    AFG      603.594404
    AGO     4134.962750
    ALB     4261.539631
    AND    39309.685362
    dtype: float64

In [39]:

    gdp.head()

Out[39]:

| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016
--- | --- | --- | --- | --- | --- | --- | ---
Country Code |  |  |  |  |  |  |
ABW | 24271.940421 | 25324.720362 | NaN | NaN | NaN | NaN | NaN
AFG | 553.300289 | 603.537023 | 669.009051 | 638.612543 | 629.345250 | 569.577923 | 561.778746
AGO | 3529.053482 | 4299.012889 | 4598.249988 | 4804.616884 | 4709.312024 | 3695.793748 | 3308.700233
ALB | 4094.358832 | 4437.178068 | 4247.614308 | 4413.081697 | 4578.666728 | 3934.895394 | 4124.982390
AND | 39736.354063 | 41098.766942 | 38391.080867 | 40619.711298 | 42294.994727 | 36038.267604 | 36988.622030

In [40]:

    population_means, population = load_file("data/population.csv")

## Creating the Plot¶

We now combine our three Series with means into one DataFrame and create our plot.

In [41]:

    data = pd.DataFrame({"gdp": gdp_means, "refugees": refugee_means/population_means}).dropna()

(Here we loose some countries with missing data.)

In [42]:

    data.plot.scatter("gdp", "refugees")
    plt.show()

We can quickly find out who the three top countries are:

In [43]:

    data.where(data["refugees"]>0.1).dropna()

Out[43]:

| gdp | refugees
--- | --- | ---
Country Code |  |
JOR | 3943.016044 | 0.313576
LBN | 8640.434951 | 0.202313
PSE | 2793.289949 | 0.484664

To improve readability:

- we switch to a log-log axis (we need to exclude countries with too small refugee numbers)
- we highlight one selected country
- We add a title

In [44]:

    ax = data[data["refugees"] > 1e-10].plot.scatter(y="refugees", x="gdp", loglog=True)
    ax = data.loc[["CHE"]].plot.scatter(y="refugees", x="gdp", ax=ax, color="r", label="Switzerland")
    plt.title("refugees fraction vs. gdp")
    plt.show()

again we can print the info for one country

In [45]:

    data.loc["CHE"]

Out[45]:

    gdp         82883.020927
    refugees        0.007429
    Name: CHE, dtype: float64

### Highlighting a Full Region¶

Based on th meta data provided by the World Bank, we can highlight a region

In [46]:

    europe = meta.loc[meta.Region == "Europe & Central Asia"].index

In [47]:

    europe[:10]

Out[47]:

    Index(['ALB', 'AND', 'ARM', 'AUT', 'AZE', 'BEL', 'BGR', 'BIH', 'BLR', 'CHE'], dtype='object', name='Country Code')

In [48]:

    ax = data[data["refugees"] > 1e-10].plot.scatter(y="refugees", x="gdp", loglog=True)
    ax = data.loc[data.index.intersection(europe)].plot.scatter(y="refugees", x="gdp", ax=ax, color="r", label="Europe & Central Asia")
    plt.title("refugees fraction vs. gdp")
    plt.show()

(As we lost some countries with missing data when we called `dropna` above, we need the `data.index.intersection`-call to select only country codes really contained in our data.)

## Fitting¶

We now look at a tiny subset of this data and look at ways to fit a function to it.

Scipy preparse a huge number of options, we will look at three options of increasing complexity and flexibility.

### Preparations¶

first we select our subset

In [49]:

    europe_small = ['AUT',
     'DEU',
     'FRA',
     'ITA',
    ]

In [50]:

    data_eu = data.loc[europe_small].dropna()

In [51]:

    ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
    plt.title("refugees fraction vs. gdp")
    plt.show()

and we create a vector with all the x values we will need to plot our fit result

In [52]:

    x = np.linspace(data_eu["gdp"].min(), data_eu["gdp"].max(), 100)

### polyfit¶

Polyfit is probably the easiest way to fit a polynome to given data.

In [53]:

    from scipy import polyfit, polyval

In [54]:

    res = polyfit(data_eu["gdp"], data_eu["refugees"],1)
    print(res)

    [ 4.09014159e-07 -1.27066203e-02]

In [55]:

    ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
    ax.plot(x, polyval(res, x))
    plt.title("refugees fraction vs. gdp")
    plt.show()

### curve_fit¶

With `curve_fit` you can define a complex fit function.

In [56]:

    from scipy.optimize import curve_fit

In [57]:

    def fit_function(x,b,c):
        return b*x+c

In [58]:

    res = curve_fit(fit_function, data_eu["gdp"], data_eu["refugees"])
    print(res)

    (array([ 4.09014159e-07, -1.27066203e-02]), array([[ 3.74367147e-16, -1.56946192e-11],
           [-1.56946192e-11,  6.67866539e-07]]))

In [59]:

    ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
    ax.plot(x, fit_function(x, *(res[0])))
    plt.title("refugees fraction vs. gdp")
    plt.show()

### leastsq¶

Finally, least-squares allows you to even specify the cost function. With this you can factor in uncertainties or weights for your data points.

In [60]:

    from scipy.optimize import leastsq

In [61]:

    def fit_function(x, p):
        return x*p[0]+p[1]

In [62]:

    def error_function(params):
        return data_eu["refugees"] - fit_function(data_eu["gdp"], params)

In [63]:

    res = leastsq(error_function, [0,0])
    print(res)

    (array([ 4.09014159e-07, -1.27066203e-02]), 2)

In [64]:

    ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
    ax.plot(x, fit_function(x, res[0]))
    plt.title("refugees fraction vs. gdp")
    plt.show()

### statsmodels¶

In [65]:

    import statsmodels.formula.api as smf

In [66]:

    res = smf.ols("refugees ~ gdp", data=data_eu).fit()

In [67]:

    print(res.summary())

                                OLS Regression Results
    ==============================================================================
    Dep. Variable:               refugees   R-squared:                       0.996
    Model:                            OLS   Adj. R-squared:                  0.993
    Method:                 Least Squares   F-statistic:                     446.9
    Date:                Mon, 24 Jun 2019   Prob (F-statistic):            0.00223
    Time:                        16:24:47   Log-Likelihood:                 29.799
    No. Observations:                   4   AIC:                            -55.60
    Df Residuals:                       2   BIC:                            -56.83
    Df Model:                           1
    Covariance Type:            nonrobust
    ==============================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
    ------------------------------------------------------------------------------
    Intercept     -0.0127      0.001    -15.548      0.004      -0.016      -0.009
    gdp          4.09e-07   1.93e-08     21.139      0.002    3.26e-07    4.92e-07
    ==============================================================================
    Omnibus:                          nan   Durbin-Watson:                   2.912
    Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.850
    Skew:                          -1.067   Prob(JB):                        0.654
    Kurtosis:                       2.261   Cond. No.                     3.47e+05
    ==============================================================================

    Warnings:
    [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    [2] The condition number is large, 3.47e+05. This might indicate that there are
    strong multicollinearity or other numerical problems.

    /usr/lib/python3/dist-packages/statsmodels/stats/stattools.py:72: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.
      "samples were given." % int(n), ValueWarning)

In [68]:

    print(res.params)

    Intercept   -1.270662e-02
    gdp          4.090142e-07
    dtype: float64

In [69]:

    ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
    ax.plot(x, res.params[1]*x+res.params[0])
    plt.title("refugees fraction vs. gdp")
    plt.show()

## Appendix: Selecting from DataFrames¶

### Accessing Rows¶

Passing a single value to `loc` returns a `Series`

In [70]:

    frame.loc["a"]

Out[70]:

    primes    11
    fibo       1
    0-4        0
    Name: a, dtype: int64

Passing a list to `loc` returns a `DataFrame` (even if the list contains a single a single value)

In [71]:

    frame.loc[["a"]]

Out[71]:

| primes | fibo | 0-4
--- | --- | --- | ---
a | 11 | 1 | 0

In [72]:

    frame.loc[["a","c"]]

Out[72]:

| primes | fibo | 0-4
--- | --- | --- | ---
a | 11 | 1 | 0
c | 17 | 2 | 2

Also slicing works (but includes the upper boundary)

In [73]:

    frame.loc["b":"d"]

Out[73]:

| primes | fibo | 0-4
--- | --- | --- | ---
b | 13 | 1 | 1
c | 17 | 2 | 2
d | 19 | 3 | 3

A list of boolean values with n-Rows entries, is considered a mask to select rows

In [74]:

    frame.loc[[True,False,True,False,True]]

Out[74]:

| primes | fibo | 0-4
--- | --- | --- | ---
a | 11 | 1 | 0
c | 17 | 2 | 2
e | 23 | 5 | 4

Instead of a list, a boolean-series can be used. Rows are matched on the index. (`frame[["primes"]] > 20` would not work as this returns a frame instead of a series.)

In [75]:

    frame.loc[frame["primes"] > 20]

Out[75]:

| primes | fibo | 0-4
--- | --- | --- | ---
e | 23 | 5 | 4

When using a mask, `.loc` is optional (but recommended to avoid confusion with columns).

In [76]:

    frame[frame["primes"] > 20]

Out[76]:

| primes | fibo | 0-4
--- | --- | --- | ---
e | 23 | 5 | 4

Using `iloc` it is possible to access rows by position as well. (without using the index)

In [77]:

    frame.iloc[2:-1]

Out[77]:

| primes | fibo | 0-4
--- | --- | --- | ---
c | 17 | 2 | 2
d | 19 | 3 | 3

### Accessing Columns¶

The frame is subscripted directly. Again, passing a singel value returns a series.

In [78]:

    frame["primes"]

Out[78]:

    a    11
    b    13
    c    17
    d    19
    e    23
    Name: primes, dtype: int64

While a list returns a `DataFrame`

In [79]:

    frame[["primes"]]

Out[79]:

| primes
--- | ---
a | 11
b | 13
c | 17
d | 19
e | 23

In [80]:

    frame[["primes","0-4"]]

Out[80]:

| primes | 0-4
--- | --- | ---
a | 11 | 0
b | 13 | 1
c | 17 | 2
d | 19 | 3
e | 23 | 4

Instead of subscripting, the `get`-method can be used.

In [81]:

    frame.get(["primes","0-4"])

Out[81]:

| primes | 0-4
--- | --- | ---
a | 11 | 0
b | 13 | 1
c | 17 | 2
d | 19 | 3
e | 23 | 4

For single columns, an attribute with the same name exists

In [82]:

    frame.primes

Out[82]:

    a    11
    b    13
    c    17
    d    19
    e    23
    Name: primes, dtype: int64

But this fails, if the column-name is not a valid attribute-name

In [83]:

    # Raises SyntaxError
    #frame.0-4

For even more options have a look at the pandas-website: https://pandas.pydata.org/pandas-docs/stable/indexing.html