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 ... 2015 2016 2017 2018 2019 2020 2021 2022 2023 Unnamed: 68
0 Aruba ABW 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
1 Africa Eastern and Southern AFE Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN ... 3333273.0 3990478.0 5155400.0 5114399.0 5087755.0 5183533.0 5436720.0 5412266.0 5553759.0 NaN
2 Afghanistan AFG Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN ... 257553.0 59770.0 75927.0 72228.0 72227.0 72278.0 66949.0 52159.0 34826.0 NaN
3 Africa Western and Central AFW Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN ... 1138010.0 1200854.0 1172523.0 1285773.0 1315229.0 1474135.0 1631057.0 1702392.0 2296159.0 NaN
4 Angola AGO Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN ... 15547.0 15547.0 41119.0 39856.0 25793.0 25791.0 26045.0 25514.0 25174.0 NaN

5 rows × 69 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 ... 2015 2016 2017 2018 2019 2020 2021 2022 2023 Unnamed: 68
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 NaN NaN NaN NaN NaN NaN
AFE Africa Eastern and Southern Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN NaN ... 3333273.0 3990478.0 5155400.0 5114399.0 5087755.0 5183533.0 5436720.0 5412266.0 5553759.0 NaN
AFG Afghanistan Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN NaN ... 257553.0 59770.0 75927.0 72228.0 72227.0 72278.0 66949.0 52159.0 34826.0 NaN
AFW Africa Western and Central Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN NaN ... 1138010.0 1200854.0 1172523.0 1285773.0 1315229.0 1474135.0 1631057.0 1702392.0 2296159.0 NaN
AGO Angola Refugee population by country or territory of ... SM.POP.REFG NaN NaN NaN NaN NaN NaN NaN ... 15547.0 15547.0 41119.0 39856.0 25793.0 25791.0 26045.0 25514.0 25174.0 NaN

5 rows × 68 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 ... 2015 2016 2017 2018 2019 2020 2021 2022 2023 Unnamed: 68
Country Code
CHE Switzerland Refugee population by country or territory of ... SM.POP.REFG 20000.0 20000.0 20000.0 20000.0 20000.0 20000.0 20500.0 ... 73326.0 82668.0 93030.0 104011.0 110162.0 115798.0 118829.0 182474.0 192507.0 NaN
DEU Germany Refugee population by country or territory of ... SM.POP.REFG 197000.0 190000.0 185000.0 182000.0 180000.0 180000.0 140000.0 ... 316098.0 669468.0 970357.0 1063835.0 1146682.0 1210596.0 1255694.0 2075445.0 2593007.0 NaN

2 rows × 68 columns

In [15]:
refugees[["1990","2000"]].head()
Out[15]:
1990 2000
Country Code
ABW NaN NaN
AFE 4709569.0 2444941.0
AFG 50.0 NaN
AFW 932052.0 968325.0
AGO 11557.0 12086.0
In [16]:
refugees.get(["1990","2000"]).head()
Out[16]:
1990 2000
Country Code
ABW NaN NaN
AFE 4709569.0 2444941.0
AFG 50.0 NaN
AFW 932052.0 968325.0
AGO 11557.0 12086.0

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(1990,2024)]]
In [18]:
che.dropna().plot()
plt.show()
No description has been provided for this image

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', '2018', '2019', '2020', '2021',
       '2022', '2023'], 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',
               '2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01',
               '2022-01-01', '2023-01-01'],
              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()
No description has been provided for this image

Removing Unwanted Data¶

We now want to create a scatter plot with refugees divided by population vs. gdp-per-captita. For each data set we will use the mean of the last 5 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

"Country Code","Region","IncomeGroup","SpecialNotes","TableName",

"ABW","Latin America & Caribbean","High income","","Aruba",

"AFE","","","26 countries, stretching from the Red Sea in the North to the Cape of Good Hope in the South (https://www.worldbank.org/en/region/afr/eastern-and-southern-africa)","Africa Eastern and Southern",

"AFG","South Asia","Low income","The reporting period for national accounts data is designated as either calendar year basis (CY) or fiscal year basis (FY). For this country, it is fiscal year-based (fiscal year-end: March 20). Also, an estimate (PA.NUS.ATLS) of the exchange rate covers the same period and thus differs from the official exchange rate (CY).

In addition, the World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In this country, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in the country. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For this country, this applies to the period 1960-2006. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.","Afghanistan",

"AFW","","","22 countries, stretching from the westernmost point of Africa, across the equator, and partly along the Atlantic Ocean till the Republic of Congo in the South (https://www.worldbank.org/en/region/afr/western-and-central-africa)","Africa Western and Central",

"AGO","Sub-Saharan Africa","Lower middle income","The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In this country, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in the country. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For this country, this applies to the period 1994-2023. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.","Angola",

"ALB","Europe & Central Asia","Upper middle income","","Albania",

"AND","Europe & Central Asia","High income","","Andorra",

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 AFE NaN
2 AFG South Asia
3 AFW NaN
4 AGO Sub-Saharan Africa
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(['AFE', 'AFW', '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', '2018', '2019', '2020', '2021', '2022', '2023',
       'Unnamed: 68'],
      dtype='object')

In addition, the last column might be missig a lot of data

In [31]:
np.sum(refugees["2023"].notnull())
Out[31]:
np.int64(165)

so we can create a list of all interesting columns

In [32]:
useful_cols = []
last_year = 2023 # depending on output above
for year in range(last_year-5,last_year+1): 
    useful_cols.append(str(year))
In [33]:
useful_cols
Out[33]:
['2018', '2019', '2020', '2021', '2022', '2023']

with this, we:

  • select the reduced dataset
  • 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[useful_cols]
    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    29851.603885
AFG      442.053744
AGO     2225.540349
ALB     6263.667603
AND    42067.093210
dtype: float64
In [39]:
gdp.head()
Out[39]:
2018 2019 2020 2021 2022 2023
Country Code
ABW 30918.483584 31902.809818 24008.127822 29127.759384 33300.838819 NaN
AFG 492.090631 497.741431 512.055098 355.777826 352.603733 NaN
AGO 2540.508879 2191.347764 1450.905111 1927.474078 2933.484644 2309.521620
ALB 5287.660801 5396.214243 5343.037704 6377.203096 6810.114041 8367.775731
AND 42904.828456 41328.600498 37207.221999 42066.490518 42350.697069 46544.720720
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()
No description has been provided for this image

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 4208.429706 0.274945
LBN 5843.601048 0.238846
PSE 3549.825962 0.488927

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()
No description has been provided for this image

again we can print the info for one country

In [45]:
data.loc["CHE"]
Out[45]:
gdp         90323.060451
refugees        0.015824
Name: CHE, dtype: float64

Highlighting a Full Region¶

Based on the 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()
No description has been provided for this image

(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()
data_eu
Out[50]:
gdp refugees
Country Code
AUT 52072.030011 0.020003
DEU 49064.065027 0.018667
FRA 41708.479489 0.007363
ITA 35016.983603 0.003547
In [51]:
ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
plt.title("refugees fraction vs. gdp")
plt.show()
No description has been provided for this image

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 numpy import polyfit, polyval
In [54]:
res = polyfit(data_eu["gdp"], data_eu["refugees"],1)
print(res)

[ 1.04719896e-06 -3.41693157e-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()
No description has been provided for this image

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([ 1.04719896e-06, -3.41693157e-02]), array([[ 2.25835729e-14, -1.00418736e-09],
       [-1.00418736e-09,  4.56445915e-05]]))
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()
No description has been provided for this image

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([ 1.04719896e-06, -3.41693157e-02]), 1)
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()
No description has been provided for this image

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:                       0.960
Model:                            OLS   Adj. R-squared:                  0.941
Method:                 Least Squares   F-statistic:                     48.56
Date:                Tue, 24 Jun 2025   Prob (F-statistic):             0.0200
Time:                        12:15:39   Log-Likelihood:                 20.583
No. Observations:                   4   AIC:                            -37.17
Df Residuals:                       2   BIC:                            -38.39
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept     -0.0342      0.007     -5.058      0.037      -0.063      -0.005
gdp         1.047e-06    1.5e-07      6.968      0.020    4.01e-07    1.69e-06
==============================================================================
Omnibus:                          nan   Durbin-Watson:                   3.329
Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.452
Skew:                          -0.507   Prob(JB):                        0.798
Kurtosis:                       1.703   Cond. No.                     3.05e+05
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.05e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

/usr/lib/python3/dist-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.
  warn("omni_normtest is not valid with less than 8 observations; %i "
In [68]:
print(res.params)

Intercept   -0.034169
gdp          0.000001
dtype: float64
In [69]:
ax = data_eu.plot.scatter(y="refugees", x="gdp", color="r")
ax.plot(x, res.params.gdp*x+res.params.Intercept)
plt.title("refugees fraction vs. gdp")
plt.show()
No description has been provided for this image

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