In [1]:
import numpy as np import pandas as pd
In [2]:
from pandas import Series, DataFrame
In [3]:
import numpy as np
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc("figure", figsize=(10, 6))
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_rows = 20
pd.options.display.max_columns = 20
pd.options.display.max_colwidth = 80
np.set_printoptions(precision=4, suppress=True)
In [4]:
obj = pd.Series([4, 7, -5, 3]) obj
In [6]:
obj2 = pd.Series([4, 7, -5, 3], index=["d", "b", "a", "c"]) obj2 obj2.index
In [7]:
obj2["a"] obj2["d"] = 6 obj2[["c", "a", "d"]]
In [8]:
obj2[obj2 > 0] obj2 * 2 import numpy as np np.exp(obj2)
In [10]:
sdata = {"Ohio": 35000, "Texas": 71000, "Oregon": 16000, "Utah": 5000}
obj3 = pd.Series(sdata)
obj3
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states = ["California", "Ohio", "Oregon", "Texas"] obj4 = pd.Series(sdata, index=states) obj4
In [13]:
pd.isna(obj4) pd.notna(obj4)
In [16]:
obj4.name = "population" obj4.index.name = "state" obj4
In [17]:
obj obj.index = ["Bob", "Steve", "Jeff", "Ryan"] obj
In [18]:
data = {"state": ["Ohio", "Ohio", "Ohio", "Nevada", "Nevada", "Nevada"],
"year": [2000, 2001, 2002, 2001, 2002, 2003],
"pop": [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
frame = pd.DataFrame(data)
In [22]:
pd.DataFrame(data, columns=["year", "state", "pop"])
In [23]:
frame2 = pd.DataFrame(data, columns=["year", "state", "pop", "debt"]) frame2 frame2.columns
In [24]:
frame2["state"] frame2.year
In [25]:
frame2.loc[1] frame2.iloc[2]
In [26]:
frame2["debt"] = 16.5 frame2 frame2["debt"] = np.arange(6.) frame2
In [27]:
val = pd.Series([-1.2, -1.5, -1.7], index=["two", "four", "five"]) frame2["debt"] = val frame2
In [28]:
frame2["eastern"] = frame2["state"] == "Ohio" frame2
In [29]:
del frame2["eastern"] frame2.columns
In [30]:
populations = {"Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6},
"Nevada": {2001: 2.4, 2002: 2.9}}
In [31]:
frame3 = pd.DataFrame(populations) frame3
In [33]:
pd.DataFrame(populations, index=[2001, 2002, 2003])
In [34]:
pdata = {"Ohio": frame3["Ohio"][:-1],
"Nevada": frame3["Nevada"][:2]}
pd.DataFrame(pdata)
In [35]:
frame3.index.name = "year" frame3.columns.name = "state" frame3
In [38]:
obj = pd.Series(np.arange(3), index=["a", "b", "c"]) index = obj.index index index[1:]
In [39]:
labels = pd.Index(np.arange(3)) labels obj2 = pd.Series([1.5, -2.5, 0], index=labels) obj2 obj2.index is labels
In [40]:
frame3 frame3.columns "Ohio" in frame3.columns 2003 in frame3.index
In [41]:
pd.Index(["foo", "foo", "bar", "bar"])
In [42]:
obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=["d", "b", "a", "c"]) obj
In [43]:
obj2 = obj.reindex(["a", "b", "c", "d", "e"]) obj2
In [44]:
obj3 = pd.Series(["blue", "purple", "yellow"], index=[0, 2, 4]) obj3 obj3.reindex(np.arange(6), method="ffill")
In [45]:
frame = pd.DataFrame(np.arange(9).reshape((3, 3)),
index=["a", "c", "d"],
columns=["Ohio", "Texas", "California"])
frame
frame2 = frame.reindex(index=["a", "b", "c", "d"])
frame2
In [46]:
states = ["Texas", "Utah", "California"] frame.reindex(columns=states)
In [47]:
frame.reindex(states, axis="columns")
In [48]:
frame.loc[["a", "d", "c"], ["California", "Texas"]]
In [49]:
obj = pd.Series(np.arange(5.), index=["a", "b", "c", "d", "e"])
obj
new_obj = obj.drop("c")
new_obj
obj.drop(["d", "c"])
In [50]:
data = pd.DataFrame(np.arange(16).reshape((4, 4)),
index=["Ohio", "Colorado", "Utah", "New York"],
columns=["one", "two", "three", "four"])
data
In [51]:
data.drop(index=["Colorado", "Ohio"])
In [52]:
data.drop(columns=["two"])
In [53]:
data.drop("two", axis=1)
data.drop(["two", "four"], axis="columns")
In [54]:
obj = pd.Series(np.arange(4.), index=["a", "b", "c", "d"]) obj obj["b"] obj[1] obj[2:4] obj[["b", "a", "d"]] obj[[1, 3]] obj[obj < 2]
In [56]:
obj1 = pd.Series([1, 2, 3], index=[2, 0, 1]) obj2 = pd.Series([1, 2, 3], index=["a", "b", "c"]) obj1 obj2 obj1[[0, 1, 2]] obj2[[0, 1, 2]]
In [57]:
obj1.iloc[[0, 1, 2]] obj2.iloc[[0, 1, 2]]
In [59]:
obj2.loc["b":"c"] = 5 obj2
In [60]:
data = pd.DataFrame(np.arange(16).reshape((4, 4)),
index=["Ohio", "Colorado", "Utah", "New York"],
columns=["one", "two", "three", "four"])
data
data["two"]
data[["three", "one"]]
In [61]:
data[:2] data[data["three"] > 5]
In [64]:
data data.loc["Colorado"]
In [65]:
data.loc[["Colorado", "New York"]]
In [66]:
data.loc["Colorado", ["two", "three"]]
In [67]:
data.iloc[2] data.iloc[[2, 1]] data.iloc[2, [3, 0, 1]] data.iloc[[1, 2], [3, 0, 1]]
In [68]:
data.loc[:"Utah", "two"] data.iloc[:, :3][data.three > 5]
In [69]:
data.loc[data.three >= 2]
In [70]:
ser = pd.Series(np.arange(3.)) ser ser[-1]
In [72]:
ser2 = pd.Series(np.arange(3.), index=["a", "b", "c"]) ser2[-1]
In [75]:
data.loc[:, "one"] = 1 data data.iloc[2] = 5 data data.loc[data["four"] > 5] = 3 data
In [76]:
data.loc[data.three == 5]["three"] = 6
In [78]:
data.loc[data.three == 5, "three"] = 6 data
In [79]:
s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index=["a", "c", "d", "e"])
s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1],
index=["a", "c", "e", "f", "g"])
s1
s2
In [81]:
df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list("bcd"),
index=["Ohio", "Texas", "Colorado"])
df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list("bde"),
index=["Utah", "Ohio", "Texas", "Oregon"])
df1
df2
In [83]:
df1 = pd.DataFrame({"A": [1, 2]})
df2 = pd.DataFrame({"B": [3, 4]})
df1
df2
df1 + df2
In [84]:
df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)),
columns=list("abcd"))
df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),
columns=list("abcde"))
df2.loc[1, "b"] = np.nan
df1
df2
In [86]:
df1.add(df2, fill_value=0)
In [88]:
df1.reindex(columns=df2.columns, fill_value=0)
In [89]:
arr = np.arange(12.).reshape((3, 4)) arr arr[0] arr - arr[0]
In [90]:
frame = pd.DataFrame(np.arange(12.).reshape((4, 3)),
columns=list("bde"),
index=["Utah", "Ohio", "Texas", "Oregon"])
series = frame.iloc[0]
frame
series
In [92]:
series2 = pd.Series(np.arange(3), index=["b", "e", "f"]) series2 frame + series2
In [93]:
series3 = frame["d"] frame series3 frame.sub(series3, axis="index")
In [94]:
frame = pd.DataFrame(np.random.standard_normal((4, 3)),
columns=list("bde"),
index=["Utah", "Ohio", "Texas", "Oregon"])
frame
np.abs(frame)
In [95]:
def f1(x):
return x.max() - x.min()
frame.apply(f1)
In [96]:
frame.apply(f1, axis="columns")
In [97]:
def f2(x):
return pd.Series([x.min(), x.max()], index=["min", "max"])
frame.apply(f2)
In [98]:
def my_format(x):
return f"{x:.2f}"
frame.applymap(my_format)
In [99]:
frame["e"].map(my_format)
In [100]:
obj = pd.Series(np.arange(4), index=["d", "a", "b", "c"]) obj obj.sort_index()
In [101]:
frame = pd.DataFrame(np.arange(8).reshape((2, 4)),
index=["three", "one"],
columns=["d", "a", "b", "c"])
frame
frame.sort_index()
frame.sort_index(axis="columns")
In [102]:
frame.sort_index(axis="columns", ascending=False)
In [103]:
obj = pd.Series([4, 7, -3, 2]) obj.sort_values()
In [104]:
obj = pd.Series([4, np.nan, 7, np.nan, -3, 2]) obj.sort_values()
In [105]:
obj.sort_values(na_position="first")
In [106]:
frame = pd.DataFrame({"b": [4, 7, -3, 2], "a": [0, 1, 0, 1]})
frame
frame.sort_values("b")
In [107]:
frame.sort_values(["a", "b"])
In [108]:
obj = pd.Series([7, -5, 7, 4, 2, 0, 4]) obj.rank()
In [110]:
obj.rank(ascending=False)
In [111]:
frame = pd.DataFrame({"b": [4.3, 7, -3, 2], "a": [0, 1, 0, 1],
"c": [-2, 5, 8, -2.5]})
frame
frame.rank(axis="columns")
In [112]:
obj = pd.Series(np.arange(5), index=["a", "a", "b", "b", "c"]) obj
In [115]:
df = pd.DataFrame(np.random.standard_normal((5, 3)),
index=["a", "a", "b", "b", "c"])
df
df.loc["b"]
df.loc["c"]
In [116]:
df = pd.DataFrame([[1.4, np.nan], [7.1, -4.5],
[np.nan, np.nan], [0.75, -1.3]],
index=["a", "b", "c", "d"],
columns=["one", "two"])
df
In [119]:
df.sum(axis="index", skipna=False) df.sum(axis="columns", skipna=False)
In [124]:
obj = pd.Series(["a", "a", "b", "c"] * 4) obj.describe()
In [125]:
price = pd.read_pickle("examples/yahoo_price.pkl")
volume = pd.read_pickle("examples/yahoo_volume.pkl")
In [126]:
returns = price.pct_change() returns.tail()
In [127]:
returns["MSFT"].corr(returns["IBM"]) returns["MSFT"].cov(returns["IBM"])
In [128]:
returns.corr() returns.cov()
In [129]:
returns.corrwith(returns["IBM"])
In [131]:
obj = pd.Series(["c", "a", "d", "a", "a", "b", "b", "c", "c"])
In [132]:
uniques = obj.unique() uniques
In [134]:
pd.value_counts(obj.to_numpy(), sort=False)
In [135]:
obj mask = obj.isin(["b", "c"]) mask obj[mask]
In [136]:
to_match = pd.Series(["c", "a", "b", "b", "c", "a"]) unique_vals = pd.Series(["c", "b", "a"]) indices = pd.Index(unique_vals).get_indexer(to_match) indices
In [137]:
data = pd.DataFrame({"Qu1": [1, 3, 4, 3, 4],
"Qu2": [2, 3, 1, 2, 3],
"Qu3": [1, 5, 2, 4, 4]})
data
In [138]:
data["Qu1"].value_counts().sort_index()
In [139]:
result = data.apply(pd.value_counts).fillna(0) result
In [140]:
data = pd.DataFrame({"a": [1, 1, 1, 2, 2], "b": [0, 0, 1, 0, 0]})
data
data.value_counts()
In [142]:
pd.options.display.max_rows = PREVIOUS_MAX_ROWS
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