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Pandas

Introduction

Pandas is a python library for data analysis and manipulation. Pandas provides two types of classes for handling data:

dates = pd.date_range("20130101", periods=6)

dates
Out[6]: 
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

Simple Methods to View Data

You can use following simple methods to view data:

df.head()

df.tail(3)

df.index

df.columns

df.to_numpy()

df.describe()

df.T

df.sort_index(axis=1, ascending=False)

df.sort_values(by="B")

df.nlargest(3, "B)

df["A"]

df[0:3]

df["20130102":"20130104"]

Simple Methods to Select Data

df.loc[dates[0]]

df.loc[:, ["A", "B"]]

df.loc["20130102":"20130104", ["A", "B"]]

df.iloc[3]

df.iloc[3:5, 0:2]

df[df["A"] > 0]

df2[df2["E"].isin(["two", "four"])]

Simple Methods to Set Data

df["F"] = s1

df.at[dates[0], "A"] = 0

df.iat[0, 1] = 0

df.loc[:, "D"] = np.array([5] * len(df))

df2[df2 > 0] = -df2

References

https://pandas.pydata.org/docs/user_guide/10min.html

https://pandas.pydata.org/docs/user_guide/cookbook.html#cookbook

Chart Visualization

The plot method allows to do basic plotting:

In [3]: np.random.seed(123456)

In [4]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))

In [5]: ts = ts.cumsum()

In [6]: ts.plot();

The Cookbook has more advanced strategies of plotting.

References

https://pandas.pydata.org/docs/user_guide/visualization.html

Table Visualization

The Styler class is returned from the DataFrame.style method.

The methods .format() and .format_index() are used to control the display value and to format it differently than the actual value. The methods are using the format spec string.

import pandas as pd
import numpy as np
import matplotlib as mpl

df = pd.DataFrame({
    "strings": ["Adam", "Mike"],
    "ints": [1, 3],
    "floats": [1.123, 1000.23]
})
df.style \
  .format(precision=3, thousands=".", decimal=",") \
  .format_index(str.upper, axis=1) \
  .relabel_index(["row 1", "row 2"], axis=0)

References

https://pandas.pydata.org/docs/user_guide/style.html