Introduction to the
In this article, we will explain the basic data types Series and DataFrame used in Pandas, and the basic behavior of creating and indexing for the two types.
To use Pandas, you need to reference the following lib:
In [1]: import numpy as np
In [2]: import pandas as pd
Copy the codeSeries
Series is a one-dimensional array with label and index. We use the following method to create a Series:
>>> s = pd.Series(data, index=index)
Copy the codeData here can be a Python dictionary, NP’s NDARray, or a scalar.
Index is a list of horizontal labels. Let’s look at how to create a Series.
fromndarraycreate
s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) s Out[67]: A-1.300797 b-2.044172 C-1.170739 D-0.445290E 1.208784 DType: Float64Copy the codeGet index with index:
s.index
Out[68]: Index(['a', 'b', 'c', 'd', 'e'], dtype='object')
Copy the codeFrom the dict create
d = {'b': 1, 'a': 0, 'c': 2}
pd.Series(d)
Out[70]:
a 0
b 1
c 2
dtype: int64
Copy the codeCreate from scalars
Pd. Series (5., index = [' a ', 'b', 'c', 'd', 'e']) Out [71] : a b c d e 5.0 5.0 5.0 5.0 5.0 dtype: float64Copy the codeThe Series and ndarray
Series and nDARray are very similar. Using index values in Series behaves like Ndarray:
S [0] Out[72]: -1.3007972194268396 s[:3] Out[73]: a-1.300797 b-2.044172 c-1.170739 dType: Float64s [s > s.median()] Out[74]: float64s [[4, 3, 1]] Out[75]: float64s [S > s.median()] Out[74]: dType: float64s [[4, 3, 1]] Out[75]: E 1.208784 D-0.445290 B-2.044172 DType: FLOAT64Copy the codeThe Series and dict
If you access a Series using a label, it behaves like a dict:
S [' a '] Out [80] : 1.3007972194268396 s = 12. [' e '] s Out [82] : A-1.300797 b-2.044172 C-1.170739 D -0.445290E 12.000000 DType: Float64Copy the codeVectorization and label alignment
Series can use a simpler vectorization operation:
S + s Out[83]: a-2.601594 b-4.088344 c-2.341477 d-0.890581 e 24.000000 dType: float64s * 2 Out[84]: A-2.601594 b-4.088344 c-2.341477 d-0.890581 e 24.000000 dType: float64 np. Exp (s) Out[85]: A 0.272315b 0.129487 c 0.310138 d 0.640638 E 162754.791419 DType: float64Copy the codeThe Name attribute
Series also has a name property that we can set at creation time:
s = pd.Series(np.random.randn(5), name='something')
s
Out[88]:
0 0.192272
1 0.110410
2 1.442358
3 -0.375792
4 1.228111
Name: something, dtype: float64
Copy the codeS also has a rename method that can rename s:
s2 = s.rename("different")
Copy the codeDataFrame
A DataFrame is a two-dimensional data structure with labels. It is made up of Series. You can think of a DataFrame as an Excel table. DataFrame can be created from the following data:
- One-dimensional Ndarrays, Lists, Dicts, or Series
- Structured array creation
- 2 d numpy. Ndarray
- Other DataFrame
From the Series to create
DataFrame can be created from a Series dictionary:
d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) df Out[92]: One two a 1.0 1.0 b 2.0 c 3.0 3.0 D NaN 4.0Copy the codeIndex reorder:
Pd.DataFrame(d, index=['d', 'b', 'a']) Out[93]: one two d NaN 4.0b 2.0 2.0a 1.0 1.0Copy the codeTo perform a rearrangement:
DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three']) Out[94]: two three d 4.0 NaN b 2.0 NaN a 1.0 NaNCopy the codeCreated from Ndarrays and lists
d = {'one': [1., 2., 3., 4.],'two': [4., 3., 2., 1.]}
pd.DataFrame(d)
Out[96]:
one two
0 1.0 4.0
1 2.0 3.0
2 3.0 2.0
3 4.0 1.0
pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
Out[97]:
one two
a 1.0 4.0
b 2.0 3.0
c 3.0 2.0
d 4.0 1.0
Copy the codeCreated from a structured array
DF can be created from a structured array:
In [47]: data = np.zeros((2, ), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')]) In [48]: data[:] = [(1, 2., 'Hello'), (2, 3., "World")] In [49]: pd.DataFrame(data) Out[49]: A B C 0 1 2.0b 'Hello' 1 2 2.0b 'World' In [50]: pd.DataFrame(data, index=['first', 'second']) Out[50]: A B C first 1 2.0b 'Hello' second 2 2.0b 'World' In [51]: pd.DataFrame(data, columns=['C', 'A', 'B']) Out[51]: C A B 0 B 'Hello' 1 2.0 1 B 'World' 2 3.0Copy the codeCreated from the dictionary list
In [52]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}] In [53]: pd.DataFrame(data2) Out[53]: In [54]: DataFrame(data2, index=['first', 'second']) Out[54]: DataFrame(data2, index=['first', 'second']) Out[54]: In [55]: data.data.columns (data2, columns=['a', 'b']) Out[55]: data.columns =['a', 'b'] Out[55]: data.columns =['a', 'b'] Out[55]: data.columns =['a', 'b'] Out[55]Copy the codeCreated from a tuple
More complex DFS can be created from tuples:
In [56]: pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2}, .... : ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4}, .... : ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6}, .... : ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8}, .... : ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}}) .... : Out[56]: a b b a c a b b 1.0 4.0 5.0 8.0 10.0 c 2.0 3.0 6.0 7.0 NaN D NaN NaN 9.0Copy the codeColumn selection, add and remove
DF can be manipulated as Series:
In [64]: df['one'] Out[64]: a 1.0b 2.0c 3.0D NaN Name: One, dType: float64 In [65]: df['three'] = df['one'] * df['two'] In [66]: df['flag'] = df['one'] > 2 In [67]: df Out[67]: One two three Flag A 1.0 1.0 1.0 False B 2.0 2.0 4.0 False C 3.0 3.0 9.0 True D NaN 4.0 NaN FalseCopy the codeYou can delete specific columns, or pop:
In [68]: del df['two']
In [69]: three = df.pop('three')
In [70]: df
Out[70]:
one flag
a 1.0 False
b 2.0 False
c 3.0 True
d NaN False
Copy the codeIf a constant is inserted, the entire column will be filled:
In [71]: df['foo'] = 'bar'
In [72]: df
Out[72]:
one flag foo
a 1.0 False bar
b 2.0 False bar
c 3.0 True bar
d NaN False bar
Copy the codeThe last column in DF is inserted by default. You can use insert to specify which column to insert into:
In [75]: df.insert(1, 'bar', df['one']) In [76]: df Out[76]: One bar Flag foo one_Trunc a 1.0 1.0 False bar 1.0B 2.0 2.0 False bar 2.0C 3.0 3.0 True bar NaN D NaN NaN False bar NaNCopy the codeUse assign to derive new columns from existing columns:
In [77]: iris = pd.read_csv('data/iris.data') In [78]: iris.head() Out[78]: SepalLength SepalWidth PetalLength PetalWidth Name 0 5.1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [79]: (iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength']) .... : .head()) .... : Out[79]: SepalLength SepalWidth PetalLength PetalWidth Name Sepal_ratio 0 5.1 3.5 1.4 0.2 Iris-setosa 0.686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris - setosa 0.720000Copy the codeNote that assign creates a new DF, leaving the original DF unchanged.
Here is a table for index and selection in DF:
| operation | grammar | Returns the result |
|---|---|---|
| Select the column | df[col] |
Series |
| Select rows by label | df.loc[label] |
Series |
| Select rows by array | df.iloc[loc] |
Series |
| The section of | df[5:10] |
DataFrame |
| Select rows using Boolean vectors | df[bool_vec] |
DataFrame |
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