Reading Tabular Data into DataFrames

Last updated on 2023-05-02 | Edit this page

Overview

Questions

  • How can I read tabular data?

Objectives

  • Import the Pandas library.
  • Use Pandas to load a simple CSV data set.
  • Get some basic information about a Pandas DataFrame.

Use the Pandas library to do statistics on tabular data.

  • Pandas is a widely-used Python library for statistics, particularly on tabular data.
  • Borrows many features from R’s dataframes.
    • A 2-dimensional table whose columns have names and potentially have different data types.
  • Load Pandas with import pandas as pd. The alias pd is commonly used to refer to the Pandas library in code.
  • Read a Comma Separated Values (CSV) data file with pd.read_csv.
    • Argument is the name of the file to be read.
    • Returns a dataframe that you can assign to a variable

PYTHON 

import pandas as pd

data_oceania = pd.read_csv('data/gapminder_gdp_oceania.csv')
print(data_oceania)

OUTPUT 

       country  gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \
0    Australia     10039.59564     10949.64959     12217.22686
1  New Zealand     10556.57566     12247.39532     13175.67800

   gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \
0     14526.12465     16788.62948     18334.19751     19477.00928
1     14463.91893     16046.03728     16233.71770     17632.41040

   gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  \
0     21888.88903     23424.76683     26997.93657     30687.75473
1     19007.19129     18363.32494     21050.41377     23189.80135

   gdpPercap_2007
0     34435.36744
1     25185.00911
  • The columns in a dataframe are the observed variables, and the rows are the observations.
  • Pandas uses backslash \ to show wrapped lines when output is too wide to fit the screen.
  • Using descriptive dataframe names helps us distinguish between multiple dataframes so we won’t accidentally overwrite a dataframe or read from the wrong one.

File Not Found

Our lessons store their data files in a data sub-directory, which is why the path to the file is data/gapminder_gdp_oceania.csv. If you forget to include data/, or if you include it but your copy of the file is somewhere else, you will get a runtime error that ends with a line like this:

ERROR 

FileNotFoundError: [Errno 2] No such file or directory: 'data/gapminder_gdp_oceania.csv'

Use index_col to specify that a column’s values should be used as row headings.

  • Row headings are numbers (0 and 1 in this case).
  • Really want to index by country.
  • Pass the name of the column to read_csv as its index_col parameter to do this.
  • Naming the dataframe data_oceania_country tells us which region the data includes (oceania) and how it is indexed (country).

PYTHON 

data_oceania_country = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')
print(data_oceania_country)

OUTPUT 

             gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \
country
Australia       10039.59564     10949.64959     12217.22686     14526.12465
New Zealand     10556.57566     12247.39532     13175.67800     14463.91893

             gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \
country
Australia       16788.62948     18334.19751     19477.00928     21888.88903
New Zealand     16046.03728     16233.71770     17632.41040     19007.19129

             gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007
country
Australia       23424.76683     26997.93657     30687.75473     34435.36744
New Zealand     18363.32494     21050.41377     23189.80135     25185.00911

Use the DataFrame.info() method to find out more about a dataframe.

PYTHON 

data_oceania_country.info()

OUTPUT 

<class 'pandas.core.frame.DataFrame'>
Index: 2 entries, Australia to New Zealand
Data columns (total 12 columns):
gdpPercap_1952    2 non-null float64
gdpPercap_1957    2 non-null float64
gdpPercap_1962    2 non-null float64
gdpPercap_1967    2 non-null float64
gdpPercap_1972    2 non-null float64
gdpPercap_1977    2 non-null float64
gdpPercap_1982    2 non-null float64
gdpPercap_1987    2 non-null float64
gdpPercap_1992    2 non-null float64
gdpPercap_1997    2 non-null float64
gdpPercap_2002    2 non-null float64
gdpPercap_2007    2 non-null float64
dtypes: float64(12)
memory usage: 208.0+ bytes
  • This is a DataFrame
  • Two rows named 'Australia' and 'New Zealand'
  • Twelve columns, each of which has two actual 64-bit floating point values.
    • We will talk later about null values, which are used to represent missing observations.
  • Uses 208 bytes of memory.

The DataFrame.columns variable stores information about the dataframe’s columns.

  • Note that this is data, not a method. (It doesn’t have parentheses.)
    • Like math.pi.
    • So do not use () to try to call it.
  • Called a member variable, or just member.

PYTHON 

print(data_oceania_country.columns)

OUTPUT 

Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',
       'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',
       'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],
      dtype='object')

Use DataFrame.T to transpose a dataframe.

  • Sometimes want to treat columns as rows and vice versa.
  • Transpose (written .T) doesn’t copy the data, just changes the program’s view of it.
  • Like columns, it is a member variable.

PYTHON 

print(data_oceania_country.T)

OUTPUT 

country           Australia  New Zealand
gdpPercap_1952  10039.59564  10556.57566
gdpPercap_1957  10949.64959  12247.39532
gdpPercap_1962  12217.22686  13175.67800
gdpPercap_1967  14526.12465  14463.91893
gdpPercap_1972  16788.62948  16046.03728
gdpPercap_1977  18334.19751  16233.71770
gdpPercap_1982  19477.00928  17632.41040
gdpPercap_1987  21888.88903  19007.19129
gdpPercap_1992  23424.76683  18363.32494
gdpPercap_1997  26997.93657  21050.41377
gdpPercap_2002  30687.75473  23189.80135
gdpPercap_2007  34435.36744  25185.00911

Use DataFrame.describe() to get summary statistics about data.

DataFrame.describe() gets the summary statistics of only the columns that have numerical data. All other columns are ignored, unless you use the argument include='all'.

PYTHON 

print(data_oceania_country.describe())

OUTPUT 

       gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \
count        2.000000        2.000000        2.000000        2.000000
mean     10298.085650    11598.522455    12696.452430    14495.021790
std        365.560078      917.644806      677.727301       43.986086
min      10039.595640    10949.649590    12217.226860    14463.918930
25%      10168.840645    11274.086022    12456.839645    14479.470360
50%      10298.085650    11598.522455    12696.452430    14495.021790
75%      10427.330655    11922.958888    12936.065215    14510.573220
max      10556.575660    12247.395320    13175.678000    14526.124650

       gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \
count         2.00000        2.000000        2.000000        2.000000
mean      16417.33338    17283.957605    18554.709840    20448.040160
std         525.09198     1485.263517     1304.328377     2037.668013
min       16046.03728    16233.717700    17632.410400    19007.191290
25%       16231.68533    16758.837652    18093.560120    19727.615725
50%       16417.33338    17283.957605    18554.709840    20448.040160
75%       16602.98143    17809.077557    19015.859560    21168.464595
max       16788.62948    18334.197510    19477.009280    21888.889030

       gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007
count        2.000000        2.000000        2.000000        2.000000
mean     20894.045885    24024.175170    26938.778040    29810.188275
std       3578.979883     4205.533703     5301.853680     6540.991104
min      18363.324940    21050.413770    23189.801350    25185.009110
25%      19628.685413    22537.294470    25064.289695    27497.598692
50%      20894.045885    24024.175170    26938.778040    29810.188275
75%      22159.406358    25511.055870    28813.266385    32122.777857
max      23424.766830    26997.936570    30687.754730    34435.367440
  • Not particularly useful with just two records, but very helpful when there are thousands.

Reading Other Data

Read the data in gapminder_gdp_americas.csv (which should be in the same directory as gapminder_gdp_oceania.csv) into a variable called data_americas and display its summary statistics.

To read in a CSV, we use pd.read_csv and pass the filename 'data/gapminder_gdp_americas.csv' to it. We also once again pass the column name 'country' to the parameter index_col in order to index by country. The summary statistics can be displayed with the DataFrame.describe() method.

PYTHON 

data_americas = pd.read_csv('data/gapminder_gdp_americas.csv', index_col='country')
data_americas.describe()

Inspecting Data

After reading the data for the Americas, use help(data_americas.head) and help(data_americas.tail) to find out what DataFrame.head and DataFrame.tail do.

  1. What method call will display the first three rows of this data?
  2. What method call will display the last three columns of this data? (Hint: you may need to change your view of the data.)
  1. We can check out the first five rows of data_americas by executing data_americas.head() which lets us view the beginning of the DataFrame. We can specify the number of rows we wish to see by specifying the parameter n in our call to data_americas.head(). To view the first three rows, execute:

PYTHON 

data_americas.head(n=3)

OUTPUT 

          continent  gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \
country
Argentina  Americas     5911.315053     6856.856212     7133.166023
Bolivia    Americas     2677.326347     2127.686326     2180.972546
Brazil     Americas     2108.944355     2487.365989     3336.585802

          gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \
country
Argentina     8052.953021     9443.038526    10079.026740     8997.897412
Bolivia       2586.886053     2980.331339     3548.097832     3156.510452
Brazil        3429.864357     4985.711467     6660.118654     7030.835878

           gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  \
country
Argentina     9139.671389     9308.418710    10967.281950     8797.640716
Bolivia       2753.691490     2961.699694     3326.143191     3413.262690
Brazil        7807.095818     6950.283021     7957.980824     8131.212843

           gdpPercap_2007
country
Argentina    12779.379640
Bolivia       3822.137084
Brazil        9065.800825
  1. To check out the last three rows of data_americas, we would use the command, americas.tail(n=3), analogous to head() used above. However, here we want to look at the last three columns so we need to change our view and then use tail(). To do so, we create a new DataFrame in which rows and columns are switched:

PYTHON 

americas_flipped = data_americas.T

We can then view the last three columns of americas by viewing the last three rows of americas_flipped:

PYTHON 

americas_flipped.tail(n=3)

OUTPUT 

country        Argentina  Bolivia   Brazil   Canada    Chile Colombia  \
gdpPercap_1997   10967.3  3326.14  7957.98  28954.9  10118.1  6117.36
gdpPercap_2002   8797.64  3413.26  8131.21    33329  10778.8  5755.26
gdpPercap_2007   12779.4  3822.14   9065.8  36319.2  13171.6  7006.58

country        Costa Rica     Cuba Dominican Republic  Ecuador    ...     \
gdpPercap_1997    6677.05  5431.99             3614.1  7429.46    ...
gdpPercap_2002    7723.45  6340.65            4563.81  5773.04    ...
gdpPercap_2007    9645.06   8948.1            6025.37  6873.26    ...

country          Mexico Nicaragua   Panama Paraguay     Peru Puerto Rico  \
gdpPercap_1997   9767.3   2253.02  7113.69   4247.4  5838.35     16999.4
gdpPercap_2002  10742.4   2474.55  7356.03  3783.67  5909.02     18855.6
gdpPercap_2007  11977.6   2749.32  9809.19  4172.84  7408.91     19328.7

country        Trinidad and Tobago United States  Uruguay Venezuela
gdpPercap_1997             8792.57       35767.4  9230.24   10165.5
gdpPercap_2002             11460.6       39097.1     7727   8605.05
gdpPercap_2007             18008.5       42951.7  10611.5   11415.8

This shows the data that we want, but we may prefer to display three columns instead of three rows, so we can flip it back:

PYTHON 

americas_flipped.tail(n=3).T

Note: we could have done the above in a single line of code by ‘chaining’ the commands:

PYTHON 

data_americas.T.tail(n=3).T

Reading Files in Other Directories

The data for your current project is stored in a file called microbes.csv, which is located in a folder called field_data. You are doing analysis in a notebook called analysis.ipynb in a sibling folder called thesis:

OUTPUT 

your_home_directory
+-- field_data/
|   +-- microbes.csv
+-- thesis/
    +-- analysis.ipynb

What value(s) should you pass to read_csv to read microbes.csv in analysis.ipynb?

We need to specify the path to the file of interest in the call to pd.read_csv. We first need to ‘jump’ out of the folder thesis using ‘../’ and then into the folder field_data using ‘field_data/’. Then we can specify the filename `microbes.csv. The result is as follows:

PYTHON 

data_microbes = pd.read_csv('../field_data/microbes.csv')

Writing Data

As well as the read_csv function for reading data from a file, Pandas provides a to_csv function to write dataframes to files. Applying what you’ve learned about reading from files, write one of your dataframes to a file called processed.csv. You can use help to get information on how to use to_csv.

In order to write the DataFrame data_americas to a file called processed.csv, execute the following command:

PYTHON 

data_americas.to_csv('processed.csv')

For help on read_csv or to_csv, you could execute, for example:

PYTHON 

help(data_americas.to_csv)
help(pd.read_csv)

Note that help(to_csv) or help(pd.to_csv) throws an error! This is due to the fact that to_csv is not a global Pandas function, but a member function of DataFrames. This means you can only call it on an instance of a DataFrame e.g., data_americas.to_csv or data_oceania.to_csv

Key Points

  • Use the Pandas library to get basic statistics out of tabular data.
  • Use index_col to specify that a column’s values should be used as row headings.
  • Use DataFrame.info to find out more about a dataframe.
  • The DataFrame.columns variable stores information about the dataframe’s columns.
  • Use DataFrame.T to transpose a dataframe.
  • Use DataFrame.describe to get summary statistics about data.